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.gitignore
vendored
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.gitignore
vendored
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app.log
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.DS_Store
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G_790000.pth
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G_790000.pth
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README.md
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README.md
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# Umamusume TTS
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Convert your text into speech using your favorite UmaMusume character's voice.
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## Prerequisites
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### Install the required packages :
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Run the following command to install dependencies:
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```
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pip install -r requirements.txt
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```
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*Note: Some packages may not be compatible with certain systems. If you encounter issues, remove the incompatible packages from `requirements.txt` and try again.*
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## Running the API
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To start the API server, run:
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```
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python app.py
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```
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## Testing the API :
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### Text-to-Speech (TTS) API
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Send a POST request to the `/synthesize` endpoint with the following payload:
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```
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curl -X POST -H "Content-Type: application/json" \
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-d '{
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"speaker_name": "Rice Shower",
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"text": "ライスね、、お兄様のこと、だーーい好き！",
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"noise_scale": 0.37,
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"noise_scale_w": 0.46,
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"length_scale": 1.3
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}' \
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http://localhost:18343/synthesize --output output.ogg
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```
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The response will be an audio file saved as `output.ogg`.
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### Get Available Speakers
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Fetch a list of available speakers with an optional search term:
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```
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curl -v -X POST http://localhost:18343/speakers -H "Content-Type: application/json" -d '{"search": "mejiro"}'
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```
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## Notes
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- Ensure your Python environment is properly set up with `pip` and other dependencies before running the application.
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- Modify the `config_path` and `checkpoint_path` in `app.py` to point to the correct configuration and model files if they are not in the default locations.
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__pycache__/commons.cpython-312.pyc
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__pycache__/constant.cpython-312.pyc
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__pycache__/constant.cpython-312.pyc
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__pycache__/utils.cpython-312.pyc
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__pycache__/utils.cpython-312.pyc
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app.py
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app.py
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import torch
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from torch import LongTensor, no_grad
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import soundfile as sf
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import base64
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import commons
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from models import utils
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from io import BytesIO
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from models.models import SynthesizerTrn
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from text import text_to_sequence
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from text.symbols import symbols
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from flask import Flask, request, jsonify, send_file
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import threading
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from constant import speakerList
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import logging
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from pydub import AudioSegment
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from pydub.utils import which
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app = Flask(__name__)
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s | %(levelname)s | %(module)s | %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S',
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handlers=[
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logging.FileHandler("app.log"), # Log to file
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logging.StreamHandler() # Log to console
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]
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)
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# Ensure Flask logs are redirected
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werkzeug_logger = logging.getLogger('werkzeug')
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werkzeug_logger.setLevel(logging.INFO)
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werkzeug_logger.addHandler(logging.FileHandler("app.log"))
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logging.getLogger('pydub.converter').setLevel(logging.WARNING)
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logger = logging.getLogger(__name__)
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logger.info("Logging is configured and ready to use.")
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# Load the model and hyperparameters when the server starts
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config_path = "configs/uma.json"
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checkpoint_path = "G_790000.pth"
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# Speaker mapping
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umas = speakerList
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# Create a mapping from speaker names to IDs
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umas_name_to_id = {name: int(idx) for idx, name in umas.items()}
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# Define a lock for thread-safe model inference
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model_lock = threading.Lock()
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# Explicitly Set FFmpeg Path for Pydub
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AudioSegment.converter = "/usr/bin/ffmpeg"
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# Debug: Print the resolved path of ffmpeg
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print("FFmpeg path:", which("ffmpeg"))
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def get_text(text, hps):
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"""
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Converts input text into a tensor format suitable for the synthesizer model.
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"""
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# Normalize text into sequence
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text_norm = text_to_sequence(text, hps.data.text_cleaners)
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if hps.data.add_blank:
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text_norm = commons.intersperse(text_norm, 0)
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return LongTensor(text_norm)
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def load_model(config_path, checkpoint_path):
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"""
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Loads the TTS model from the given configuration and checkpoint files.
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"""
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# Load hyperparameters from JSON config
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hps = utils.get_hparams_from_file(config_path)
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# Initialize the VITS model
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net_g = SynthesizerTrn(
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n_vocab=len(symbols),
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spec_channels=hps.data.filter_length // 2 + 1,
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segment_size=hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model
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)
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net_g.eval()
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# Move model to GPU if available
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if torch.cuda.is_available():
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net_g.cuda()
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else:
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logger.warning("CUDA is not available. Running on CPU.")
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# Load model checkpoint
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utils.load_checkpoint(checkpoint_path, net_g, None)
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logger.info(f"Model checkpoint '{checkpoint_path}' loaded successfully!")
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return net_g, hps
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def synthesize_speech(net_g, hps, text, speaker_id, noise_scale, noise_scale_w, length_scale):
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"""
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Generates speech audio from text using the TTS model.
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"""
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stn_tst = get_text(text, hps)
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x_tst = stn_tst.unsqueeze(0)
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x_tst_lengths = LongTensor([stn_tst.size(0)])
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sid = LongTensor([speaker_id]) # Ensure speaker_id is an integer
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if torch.cuda.is_available():
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x_tst = x_tst.cuda()
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x_tst_lengths = x_tst_lengths.cuda()
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sid = sid.cuda()
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with no_grad():
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audio = net_g.infer(
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x_tst,
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x_tst_lengths,
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sid=sid,
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noise_scale=noise_scale,
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noise_scale_w=noise_scale_w,
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length_scale=length_scale
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)[0][0,0].data.cpu().float().numpy()
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return audio
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# Loading the models on server boot
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net_g, hps = load_model(config_path, checkpoint_path)
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@app.route('/synthesize', methods=['POST'])
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def synthesize():
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data = request.get_json()
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# Log all request parameters
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logger.info(f"Request parameters: {data}")
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# Extract parameters with default values
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Speaker_Uma = data.get('speaker_name', 'Rice Shower')
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Japanese_text = data.get('text', 'おにー様、すきです')
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noise_scale = float(data.get('noise_scale', 0.37))
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noise_scale_w = float(data.get('noise_scale_w', 0.46))
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length_scale = float(data.get('length_scale', 1.3))
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# Get speaker ID from name
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speaker_id = umas_name_to_id.get(Speaker_Uma)
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if speaker_id is None:
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logger.error(f"Speaker '{Speaker_Uma}' not found.")
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return jsonify({'error': f"Speaker '{Speaker_Uma}' not found."}), 400
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# Generate speech audio
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logger.info(f"Generating synthesis for speaker: {Speaker_Uma}")
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with model_lock:
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audio = synthesize_speech(
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net_g, hps, Japanese_text, speaker_id, noise_scale, noise_scale_w, length_scale
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)
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# Save the audio as WAV in memory
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wav_buffer = BytesIO()
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sf.write(wav_buffer, audio, hps.data.sampling_rate, format='WAV')
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wav_buffer.seek(0)
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# Convert WAV to AAC (m4a format) using pydub
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wav_audio = AudioSegment.from_file(wav_buffer, format="wav")
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aac_buffer = BytesIO()
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wav_audio.export(aac_buffer, format="ipod")
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aac_buffer.seek(0)
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logger.info("Audio synthesis completed successfully.")
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logger.info("Sending synthesized audio file")
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# Return the audio file
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return send_file(
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aac_buffer,
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mimetype='audio/aac',
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as_attachment=True,
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download_name='output.aac'
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)
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@app.route('/speakers', methods=['POST'])
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def get_speakers():
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"""
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API endpoint to retrieve a list of speakers.
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Expects a JSON payload with an optional 'search' parameter.
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"""
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# Parse the JSON payload
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data = request.get_json()
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# Log all request parameters
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logger.info(f"Request parameters: {data}")
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search = data.get('search', '') if data else ''
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# Filter speakers based on the search term
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speaker_list = [name for name in umas.values() if not search or search.lower() in name.lower()]
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# Return the filtered list as JSON
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logger.info(f"Sending speaker list for search term: '{search}' with {len(speaker_list)} results.")
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return jsonify(speaker_list)
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=18343)
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commons.py
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commons.py
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import math
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size*dilation - dilation)/2)
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def convert_pad_shape(pad_shape):
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l = pad_shape[::-1]
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pad_shape = [item for sublist in l for item in sublist]
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return pad_shape
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def intersperse(lst, item):
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result = [item] * (len(lst) * 2 + 1)
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result[1::2] = lst
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return result
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def kl_divergence(m_p, logs_p, m_q, logs_q):
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"""KL(P||Q)"""
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kl = (logs_q - logs_p) - 0.5
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kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
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return kl
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def rand_gumbel(shape):
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"""Sample from the Gumbel distribution, protect from overflows."""
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uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
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return -torch.log(-torch.log(uniform_samples))
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def rand_gumbel_like(x):
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g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
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return g
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def slice_segments(x, ids_str, segment_size=4):
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ret = torch.zeros_like(x[:, :, :segment_size])
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for i in range(x.size(0)):
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idx_str = ids_str[i]
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idx_end = idx_str + segment_size
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ret[i] = x[i, :, idx_str:idx_end]
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return ret
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def rand_slice_segments(x, x_lengths=None, segment_size=4):
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b, d, t = x.size()
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if x_lengths is None:
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x_lengths = t
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ids_str_max = x_lengths - segment_size + 1
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ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
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ret = slice_segments(x, ids_str, segment_size)
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return ret, ids_str
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def get_timing_signal_1d(
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length, channels, min_timescale=1.0, max_timescale=1.0e4):
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position = torch.arange(length, dtype=torch.float)
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num_timescales = channels // 2
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log_timescale_increment = (
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math.log(float(max_timescale) / float(min_timescale)) /
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(num_timescales - 1))
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inv_timescales = min_timescale * torch.exp(
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torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
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scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
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signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
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signal = F.pad(signal, [0, 0, 0, channels % 2])
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signal = signal.view(1, channels, length)
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return signal
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def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
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b, channels, length = x.size()
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signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
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return x + signal.to(dtype=x.dtype, device=x.device)
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def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
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b, channels, length = x.size()
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signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
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return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
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def subsequent_mask(length):
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mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
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return mask
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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def convert_pad_shape(pad_shape):
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l = pad_shape[::-1]
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pad_shape = [item for sublist in l for item in sublist]
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return pad_shape
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def shift_1d(x):
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x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
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return x
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def sequence_mask(length, max_length=None):
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if max_length is None:
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max_length = length.max()
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x = torch.arange(max_length, dtype=length.dtype, device=length.device)
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return x.unsqueeze(0) < length.unsqueeze(1)
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def generate_path(duration, mask):
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"""
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duration: [b, 1, t_x]
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mask: [b, 1, t_y, t_x]
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"""
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device = duration.device
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b, _, t_y, t_x = mask.shape
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cum_duration = torch.cumsum(duration, -1)
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cum_duration_flat = cum_duration.view(b * t_x)
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path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
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path = path.view(b, t_x, t_y)
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path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
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path = path.unsqueeze(1).transpose(2,3) * mask
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return path
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def clip_grad_value_(parameters, clip_value, norm_type=2):
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = list(filter(lambda p: p.grad is not None, parameters))
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norm_type = float(norm_type)
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if clip_value is not None:
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clip_value = float(clip_value)
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total_norm = 0
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for p in parameters:
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param_norm = p.grad.data.norm(norm_type)
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total_norm += param_norm.item() ** norm_type
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if clip_value is not None:
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p.grad.data.clamp_(min=-clip_value, max=clip_value)
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total_norm = total_norm ** (1. / norm_type)
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return total_norm
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52
configs/ljs_base.json
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52
configs/ljs_base.json
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{
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||||
"train": {
|
||||
"log_interval": 200,
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||||
"eval_interval": 1000,
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"seed": 1234,
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||||
"epochs": 20000,
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"learning_rate": 2e-4,
|
||||
"betas": [0.8, 0.99],
|
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"eps": 1e-9,
|
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"batch_size": 64,
|
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"fp16_run": true,
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"lr_decay": 0.999875,
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"segment_size": 8192,
|
||||
"init_lr_ratio": 1,
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||||
"warmup_epochs": 0,
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"c_mel": 45,
|
||||
"c_kl": 1.0
|
||||
},
|
||||
"data": {
|
||||
"training_files":"filelists/ljs_audio_text_train_filelist.txt.cleaned",
|
||||
"validation_files":"filelists/ljs_audio_text_val_filelist.txt.cleaned",
|
||||
"text_cleaners":["english_cleaners2"],
|
||||
"max_wav_value": 32768.0,
|
||||
"sampling_rate": 22050,
|
||||
"filter_length": 1024,
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
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||||
"n_mel_channels": 80,
|
||||
"mel_fmin": 0.0,
|
||||
"mel_fmax": null,
|
||||
"add_blank": true,
|
||||
"n_speakers": 0,
|
||||
"cleaned_text": true
|
||||
},
|
||||
"model": {
|
||||
"inter_channels": 192,
|
||||
"hidden_channels": 192,
|
||||
"filter_channels": 768,
|
||||
"n_heads": 2,
|
||||
"n_layers": 6,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": 0.1,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3,7,11],
|
||||
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
||||
"upsample_rates": [8,8,2,2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16,16,4,4],
|
||||
"n_layers_q": 3,
|
||||
"use_spectral_norm": false
|
||||
}
|
||||
}
|
||||
53
configs/ljs_nosdp.json
Normal file
53
configs/ljs_nosdp.json
Normal file
@ -0,0 +1,53 @@
|
||||
{
|
||||
"train": {
|
||||
"log_interval": 200,
|
||||
"eval_interval": 1000,
|
||||
"seed": 1234,
|
||||
"epochs": 20000,
|
||||
"learning_rate": 2e-4,
|
||||
"betas": [0.8, 0.99],
|
||||
"eps": 1e-9,
|
||||
"batch_size": 64,
|
||||
"fp16_run": true,
|
||||
"lr_decay": 0.999875,
|
||||
"segment_size": 8192,
|
||||
"init_lr_ratio": 1,
|
||||
"warmup_epochs": 0,
|
||||
"c_mel": 45,
|
||||
"c_kl": 1.0
|
||||
},
|
||||
"data": {
|
||||
"training_files":"filelists/ljs_audio_text_train_filelist.txt.cleaned",
|
||||
"validation_files":"filelists/ljs_audio_text_val_filelist.txt.cleaned",
|
||||
"text_cleaners":["english_cleaners2"],
|
||||
"max_wav_value": 32768.0,
|
||||
"sampling_rate": 22050,
|
||||
"filter_length": 1024,
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
|
||||
"n_mel_channels": 80,
|
||||
"mel_fmin": 0.0,
|
||||
"mel_fmax": null,
|
||||
"add_blank": true,
|
||||
"n_speakers": 0,
|
||||
"cleaned_text": true
|
||||
},
|
||||
"model": {
|
||||
"inter_channels": 192,
|
||||
"hidden_channels": 192,
|
||||
"filter_channels": 768,
|
||||
"n_heads": 2,
|
||||
"n_layers": 6,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": 0.1,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3,7,11],
|
||||
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
||||
"upsample_rates": [8,8,2,2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16,16,4,4],
|
||||
"n_layers_q": 3,
|
||||
"use_spectral_norm": false,
|
||||
"use_sdp": false
|
||||
}
|
||||
}
|
||||
53
configs/uma.json
Normal file
53
configs/uma.json
Normal file
@ -0,0 +1,53 @@
|
||||
{
|
||||
"train": {
|
||||
"log_interval": 200,
|
||||
"eval_interval": 1000,
|
||||
"seed": 1234,
|
||||
"epochs": 10000,
|
||||
"learning_rate": 2e-4,
|
||||
"betas": [0.8, 0.99],
|
||||
"eps": 1e-9,
|
||||
"batch_size": 12,
|
||||
"fp16_run": true,
|
||||
"lr_decay": 0.999875,
|
||||
"segment_size": 8192,
|
||||
"init_lr_ratio": 1,
|
||||
"warmup_epochs": 0,
|
||||
"c_mel": 45,
|
||||
"c_kl": 1.0
|
||||
},
|
||||
"data": {
|
||||
"training_files":"filelists/uma_text_train.txt.cleaned",
|
||||
"validation_files":"filelists/uma_text_val.txt.cleaned",
|
||||
"text_cleaners":["japanese_cleaners"],
|
||||
"max_wav_value": 32768.0,
|
||||
"sampling_rate": 22050,
|
||||
"filter_length": 1024,
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
|
||||
"n_mel_channels": 80,
|
||||
"mel_fmin": 0.0,
|
||||
"mel_fmax": null,
|
||||
"add_blank": true,
|
||||
"n_speakers": 92,
|
||||
"cleaned_text": true
|
||||
},
|
||||
"model": {
|
||||
"inter_channels": 192,
|
||||
"hidden_channels": 192,
|
||||
"filter_channels": 768,
|
||||
"n_heads": 2,
|
||||
"n_layers": 6,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": 0.1,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3,7,11],
|
||||
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
||||
"upsample_rates": [8,8,2,2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16,16,4,4],
|
||||
"n_layers_q": 3,
|
||||
"use_spectral_norm": false,
|
||||
"gin_channels": 256
|
||||
}
|
||||
}
|
||||
53
configs/vctk_base.json
Normal file
53
configs/vctk_base.json
Normal file
@ -0,0 +1,53 @@
|
||||
{
|
||||
"train": {
|
||||
"log_interval": 200,
|
||||
"eval_interval": 1000,
|
||||
"seed": 1234,
|
||||
"epochs": 10000,
|
||||
"learning_rate": 2e-4,
|
||||
"betas": [0.8, 0.99],
|
||||
"eps": 1e-9,
|
||||
"batch_size": 64,
|
||||
"fp16_run": true,
|
||||
"lr_decay": 0.999875,
|
||||
"segment_size": 8192,
|
||||
"init_lr_ratio": 1,
|
||||
"warmup_epochs": 0,
|
||||
"c_mel": 45,
|
||||
"c_kl": 1.0
|
||||
},
|
||||
"data": {
|
||||
"training_files":"filelists/vctk_audio_sid_text_train_filelist.txt.cleaned",
|
||||
"validation_files":"filelists/vctk_audio_sid_text_val_filelist.txt.cleaned",
|
||||
"text_cleaners":["english_cleaners2"],
|
||||
"max_wav_value": 32768.0,
|
||||
"sampling_rate": 22050,
|
||||
"filter_length": 1024,
|
||||
"hop_length": 256,
|
||||
"win_length": 1024,
|
||||
"n_mel_channels": 80,
|
||||
"mel_fmin": 0.0,
|
||||
"mel_fmax": null,
|
||||
"add_blank": true,
|
||||
"n_speakers": 109,
|
||||
"cleaned_text": true
|
||||
},
|
||||
"model": {
|
||||
"inter_channels": 192,
|
||||
"hidden_channels": 192,
|
||||
"filter_channels": 768,
|
||||
"n_heads": 2,
|
||||
"n_layers": 6,
|
||||
"kernel_size": 3,
|
||||
"p_dropout": 0.1,
|
||||
"resblock": "1",
|
||||
"resblock_kernel_sizes": [3,7,11],
|
||||
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
||||
"upsample_rates": [8,8,2,2],
|
||||
"upsample_initial_channel": 512,
|
||||
"upsample_kernel_sizes": [16,16,4,4],
|
||||
"n_layers_q": 3,
|
||||
"use_spectral_norm": false,
|
||||
"gin_channels": 256
|
||||
}
|
||||
}
|
||||
93
constant.py
Normal file
93
constant.py
Normal file
@ -0,0 +1,93 @@
|
||||
speakerList = {
|
||||
"0": "Special Week",
|
||||
"1": "Silence Suzuka",
|
||||
"2": "Tokai Teio",
|
||||
"3": "Maruzensky",
|
||||
"4": "Fuji Kiseki",
|
||||
"5": "Oguri Cap",
|
||||
"6": "Gold Ship",
|
||||
"7": "Vodka",
|
||||
"8": "Daiwa Scarlet",
|
||||
"9": "Taiki Shuttle",
|
||||
"10": "Grass Wonder",
|
||||
"11": "Hishi Amazon",
|
||||
"12": "Mejiro McQueen",
|
||||
"13": "El Condor Pasa",
|
||||
"14": "TM Opera O",
|
||||
"15": "Narita Brian",
|
||||
"16": "Symboli Rudolf",
|
||||
"17": "Air Groove",
|
||||
"18": "Agnes Digital",
|
||||
"19": "Seiun Sky",
|
||||
"20": "Tamamo Cross",
|
||||
"21": "Fine Motion",
|
||||
"22": "Biwa Hayahide",
|
||||
"23": "Mayano Top Gun",
|
||||
"24": "Manhattan Cafe",
|
||||
"25": "Mihono Bourbon",
|
||||
"26": "Mejiro Ryan",
|
||||
"27": "Hishi Akebono",
|
||||
"28": "Yukino Bijin",
|
||||
"29": "Rice Shower",
|
||||
"30": "Ines Fujin",
|
||||
"31": "Agnes Tachyon",
|
||||
"32": "Admire Vega",
|
||||
"33": "Inari One",
|
||||
"34": "Winning Ticket",
|
||||
"35": "Air Shakur",
|
||||
"36": "Eishin Flash",
|
||||
"37": "Curren Chan",
|
||||
"38": "Kawakami Princess",
|
||||
"39": "Gold City",
|
||||
"40": "Sakura Bakushin O",
|
||||
"41": "Seeking the Pearl",
|
||||
"42": "Shinko Windy",
|
||||
"43": "Sweep Tosho",
|
||||
"44": "Super Creek",
|
||||
"45": "Smart Falcon",
|
||||
"46": "Zenno Rob Roy",
|
||||
"47": "Tosen Jordan",
|
||||
"48": "Nakayama Festa",
|
||||
"49": "Narita Taishin",
|
||||
"50": "Nishino Flower",
|
||||
"51": "Haru Urara",
|
||||
"52": "Bamboo Memory",
|
||||
"53": "Biko Pegasus",
|
||||
"54": "Marvelous Sunday",
|
||||
"55": "Matikanefukukitaru",
|
||||
"56": "Mr. C.B.",
|
||||
"57": "Meisho doto",
|
||||
"58": "Mejiro Dober",
|
||||
"59": "Nice Nature",
|
||||
"60": "King Halo",
|
||||
"61": "Machikane Tannhauser",
|
||||
"62": "Ikuno Dictus",
|
||||
"63": "Mejiro Palmer",
|
||||
"64": "Daitaku Helios",
|
||||
"65": "Twin Turbo",
|
||||
"66": "Satono Diamond",
|
||||
"67": "Kitasan Black",
|
||||
"68": "Sakura Chiyono O",
|
||||
"69": "Sirius Symboli",
|
||||
"70": "Mejiro Ardan",
|
||||
"71": "Yaeno Muteki",
|
||||
"72": "Tsurumaru Tsuyoshi",
|
||||
"73": "Mejiro Bright",
|
||||
"75": "Sakura Laurel",
|
||||
"76": "Narita Top Road",
|
||||
"77": "Yamanin Zephyr",
|
||||
"78": "Daiichi Ruby",
|
||||
"79": "Aston Machan",
|
||||
"80": "K.S. Miracle",
|
||||
"81": "Copano Rickey",
|
||||
"82": "Hokko Tarumae",
|
||||
"83": "Wonder Acute",
|
||||
"84": "Montjeu",
|
||||
"85": "Hayakawa Tazuna",
|
||||
"86": "Akikawa Yayoi(President)",
|
||||
"87": "Otonasi Etuko",
|
||||
"88": "Kiryuin Aoi",
|
||||
"89": "Anshinzawa Sasami",
|
||||
"90": "Kashimoto Rico",
|
||||
"91": "Light Hello"
|
||||
}
|
||||
BIN
models/__pycache__/attentions.cpython-312.pyc
Normal file
BIN
models/__pycache__/attentions.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/models.cpython-312.pyc
Normal file
BIN
models/__pycache__/models.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/modules.cpython-312.pyc
Normal file
BIN
models/__pycache__/modules.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/transforms.cpython-312.pyc
Normal file
BIN
models/__pycache__/transforms.cpython-312.pyc
Normal file
Binary file not shown.
BIN
models/__pycache__/utils.cpython-312.pyc
Normal file
BIN
models/__pycache__/utils.cpython-312.pyc
Normal file
Binary file not shown.
303
models/attentions.py
Normal file
303
models/attentions.py
Normal file
@ -0,0 +1,303 @@
|
||||
import copy
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import commons
|
||||
from models import modules
|
||||
from models.modules import LayerNorm
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.window_size = window_size
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.attn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_2 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
||||
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
y = self.attn_layers[i](x, x, attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_2[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.self_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_0 = nn.ModuleList()
|
||||
self.encdec_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_2 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
|
||||
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
||||
self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
||||
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask, h, h_mask):
|
||||
"""
|
||||
x: decoder input
|
||||
h: encoder output
|
||||
"""
|
||||
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_0[i](x + y)
|
||||
|
||||
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_2[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
|
||||
super().__init__()
|
||||
assert channels % n_heads == 0
|
||||
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels
|
||||
self.n_heads = n_heads
|
||||
self.p_dropout = p_dropout
|
||||
self.window_size = window_size
|
||||
self.heads_share = heads_share
|
||||
self.block_length = block_length
|
||||
self.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
self.attn = None
|
||||
|
||||
self.k_channels = channels // n_heads
|
||||
self.conv_q = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_k = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_v = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if window_size is not None:
|
||||
n_heads_rel = 1 if heads_share else n_heads
|
||||
rel_stddev = self.k_channels**-0.5
|
||||
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
||||
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
||||
|
||||
nn.init.xavier_uniform_(self.conv_q.weight)
|
||||
nn.init.xavier_uniform_(self.conv_k.weight)
|
||||
nn.init.xavier_uniform_(self.conv_v.weight)
|
||||
if proximal_init:
|
||||
with torch.no_grad():
|
||||
self.conv_k.weight.copy_(self.conv_q.weight)
|
||||
self.conv_k.bias.copy_(self.conv_q.bias)
|
||||
|
||||
def forward(self, x, c, attn_mask=None):
|
||||
q = self.conv_q(x)
|
||||
k = self.conv_k(c)
|
||||
v = self.conv_v(c)
|
||||
|
||||
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
||||
|
||||
x = self.conv_o(x)
|
||||
return x
|
||||
|
||||
def attention(self, query, key, value, mask=None):
|
||||
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
||||
b, d, t_s, t_t = (*key.size(), query.size(2))
|
||||
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
||||
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
||||
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
||||
|
||||
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
||||
if self.window_size is not None:
|
||||
assert t_s == t_t, "Relative attention is only available for self-attention."
|
||||
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
||||
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
||||
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
||||
scores = scores + scores_local
|
||||
if self.proximal_bias:
|
||||
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
||||
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
||||
if mask is not None:
|
||||
scores = scores.masked_fill(mask == 0, -1e4)
|
||||
if self.block_length is not None:
|
||||
assert t_s == t_t, "Local attention is only available for self-attention."
|
||||
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
||||
scores = scores.masked_fill(block_mask == 0, -1e4)
|
||||
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
||||
p_attn = self.drop(p_attn)
|
||||
output = torch.matmul(p_attn, value)
|
||||
if self.window_size is not None:
|
||||
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
||||
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
||||
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
||||
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
||||
return output, p_attn
|
||||
|
||||
def _matmul_with_relative_values(self, x, y):
|
||||
"""
|
||||
x: [b, h, l, m]
|
||||
y: [h or 1, m, d]
|
||||
ret: [b, h, l, d]
|
||||
"""
|
||||
ret = torch.matmul(x, y.unsqueeze(0))
|
||||
return ret
|
||||
|
||||
def _matmul_with_relative_keys(self, x, y):
|
||||
"""
|
||||
x: [b, h, l, d]
|
||||
y: [h or 1, m, d]
|
||||
ret: [b, h, l, m]
|
||||
"""
|
||||
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
||||
return ret
|
||||
|
||||
def _get_relative_embeddings(self, relative_embeddings, length):
|
||||
max_relative_position = 2 * self.window_size + 1
|
||||
# Pad first before slice to avoid using cond ops.
|
||||
pad_length = max(length - (self.window_size + 1), 0)
|
||||
slice_start_position = max((self.window_size + 1) - length, 0)
|
||||
slice_end_position = slice_start_position + 2 * length - 1
|
||||
if pad_length > 0:
|
||||
padded_relative_embeddings = F.pad(
|
||||
relative_embeddings,
|
||||
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
||||
else:
|
||||
padded_relative_embeddings = relative_embeddings
|
||||
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
||||
return used_relative_embeddings
|
||||
|
||||
def _relative_position_to_absolute_position(self, x):
|
||||
"""
|
||||
x: [b, h, l, 2*l-1]
|
||||
ret: [b, h, l, l]
|
||||
"""
|
||||
batch, heads, length, _ = x.size()
|
||||
# Concat columns of pad to shift from relative to absolute indexing.
|
||||
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
||||
|
||||
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
||||
x_flat = x.view([batch, heads, length * 2 * length])
|
||||
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
||||
|
||||
# Reshape and slice out the padded elements.
|
||||
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
||||
return x_final
|
||||
|
||||
def _absolute_position_to_relative_position(self, x):
|
||||
"""
|
||||
x: [b, h, l, l]
|
||||
ret: [b, h, l, 2*l-1]
|
||||
"""
|
||||
batch, heads, length, _ = x.size()
|
||||
# padd along column
|
||||
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
||||
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
||||
# add 0's in the beginning that will skew the elements after reshape
|
||||
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
||||
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
||||
return x_final
|
||||
|
||||
def _attention_bias_proximal(self, length):
|
||||
"""Bias for self-attention to encourage attention to close positions.
|
||||
Args:
|
||||
length: an integer scalar.
|
||||
Returns:
|
||||
a Tensor with shape [1, 1, length, length]
|
||||
"""
|
||||
r = torch.arange(length, dtype=torch.float32)
|
||||
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
||||
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
||||
|
||||
|
||||
class FFN(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.activation = activation
|
||||
self.causal = causal
|
||||
|
||||
if causal:
|
||||
self.padding = self._causal_padding
|
||||
else:
|
||||
self.padding = self._same_padding
|
||||
|
||||
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
||||
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x = self.conv_1(self.padding(x * x_mask))
|
||||
if self.activation == "gelu":
|
||||
x = x * torch.sigmoid(1.702 * x)
|
||||
else:
|
||||
x = torch.relu(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(self.padding(x * x_mask))
|
||||
return x * x_mask
|
||||
|
||||
def _causal_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = self.kernel_size - 1
|
||||
pad_r = 0
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
|
||||
def _same_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = (self.kernel_size - 1) // 2
|
||||
pad_r = self.kernel_size // 2
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
533
models/models.py
Normal file
533
models/models.py
Normal file
@ -0,0 +1,533 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import commons
|
||||
from models import modules, attentions
|
||||
import monotonic_align
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from commons import init_weights, get_padding
|
||||
|
||||
|
||||
class StochasticDurationPredictor(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
|
||||
super().__init__()
|
||||
filter_channels = in_channels # it needs to be removed from future version.
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.log_flow = modules.Log()
|
||||
self.flows = nn.ModuleList()
|
||||
self.flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
self.post_pre = nn.Conv1d(1, filter_channels, 1)
|
||||
self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
||||
self.post_flows = nn.ModuleList()
|
||||
self.post_flows.append(modules.ElementwiseAffine(2))
|
||||
for i in range(4):
|
||||
self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
|
||||
self.post_flows.append(modules.Flip())
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, filter_channels, 1)
|
||||
self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
|
||||
self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
|
||||
x = torch.detach(x)
|
||||
x = self.pre(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.convs(x, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
|
||||
if not reverse:
|
||||
flows = self.flows
|
||||
assert w is not None
|
||||
|
||||
logdet_tot_q = 0
|
||||
h_w = self.post_pre(w)
|
||||
h_w = self.post_convs(h_w, x_mask)
|
||||
h_w = self.post_proj(h_w) * x_mask
|
||||
e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
|
||||
z_q = e_q
|
||||
for flow in self.post_flows:
|
||||
z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
|
||||
logdet_tot_q += logdet_q
|
||||
z_u, z1 = torch.split(z_q, [1, 1], 1)
|
||||
u = torch.sigmoid(z_u) * x_mask
|
||||
z0 = (w - u) * x_mask
|
||||
logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
|
||||
logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
|
||||
|
||||
logdet_tot = 0
|
||||
z0, logdet = self.log_flow(z0, x_mask)
|
||||
logdet_tot += logdet
|
||||
z = torch.cat([z0, z1], 1)
|
||||
for flow in flows:
|
||||
z, logdet = flow(z, x_mask, g=x, reverse=reverse)
|
||||
logdet_tot = logdet_tot + logdet
|
||||
nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
|
||||
return nll + logq # [b]
|
||||
else:
|
||||
flows = list(reversed(self.flows))
|
||||
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
|
||||
z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
|
||||
for flow in flows:
|
||||
z = flow(z, x_mask, g=x, reverse=reverse)
|
||||
z0, z1 = torch.split(z, [1, 1], 1)
|
||||
logw = z0
|
||||
return logw
|
||||
|
||||
|
||||
class DurationPredictor(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
||||
self.norm_1 = modules.LayerNorm(filter_channels)
|
||||
self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
|
||||
self.norm_2 = modules.LayerNorm(filter_channels)
|
||||
self.proj = nn.Conv1d(filter_channels, 1, 1)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, in_channels, 1)
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
x = torch.detach(x)
|
||||
if g is not None:
|
||||
g = torch.detach(g)
|
||||
x = x + self.cond(g)
|
||||
x = self.conv_1(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_1(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(x * x_mask)
|
||||
x = torch.relu(x)
|
||||
x = self.norm_2(x)
|
||||
x = self.drop(x)
|
||||
x = self.proj(x * x_mask)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
n_vocab,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout):
|
||||
super().__init__()
|
||||
self.n_vocab = n_vocab
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.emb = nn.Embedding(n_vocab, hidden_channels)
|
||||
nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
|
||||
|
||||
self.encoder = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths):
|
||||
x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
|
||||
x = torch.transpose(x, 1, -1) # [b, h, t]
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
|
||||
x = self.encoder(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
return x, m, logs, x_mask
|
||||
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class PosteriorEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
|
||||
super(Generator, self).__init__()
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
|
||||
resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(weight_norm(
|
||||
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
||||
k, u, padding=(k-u)//2)))
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel//(2**(i+1))
|
||||
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
|
||||
self.resblocks.append(resblock(ch, k, d))
|
||||
|
||||
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
|
||||
self.ups.apply(init_weights)
|
||||
|
||||
if gin_channels != 0:
|
||||
self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
|
||||
|
||||
def forward(self, x, g=None):
|
||||
x = self.conv_pre(x)
|
||||
if g is not None:
|
||||
x = x + self.cond(g)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i*self.num_kernels+j](x)
|
||||
else:
|
||||
xs += self.resblocks[i*self.num_kernels+j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2,3,5,7,11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
n_vocab,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
n_speakers=0,
|
||||
gin_channels=0,
|
||||
use_sdp=True,
|
||||
**kwargs):
|
||||
|
||||
super().__init__()
|
||||
self.n_vocab = n_vocab
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.n_speakers = n_speakers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.use_sdp = use_sdp
|
||||
|
||||
self.enc_p = TextEncoder(n_vocab,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
|
||||
self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
|
||||
if use_sdp:
|
||||
self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
|
||||
else:
|
||||
self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
|
||||
|
||||
if n_speakers > 1:
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
def forward(self, x, x_lengths, y, y_lengths, sid=None):
|
||||
|
||||
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
||||
if self.n_speakers > 0:
|
||||
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
||||
else:
|
||||
g = None
|
||||
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
|
||||
z_p = self.flow(z, y_mask, g=g)
|
||||
|
||||
with torch.no_grad():
|
||||
# negative cross-entropy
|
||||
s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
|
||||
neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
|
||||
neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
||||
neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
|
||||
neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
|
||||
neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
|
||||
|
||||
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
||||
attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
|
||||
|
||||
w = attn.sum(2)
|
||||
if self.use_sdp:
|
||||
l_length = self.dp(x, x_mask, w, g=g)
|
||||
l_length = l_length / torch.sum(x_mask)
|
||||
else:
|
||||
logw_ = torch.log(w + 1e-6) * x_mask
|
||||
logw = self.dp(x, x_mask, g=g)
|
||||
l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
|
||||
|
||||
# expand prior
|
||||
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
|
||||
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
|
||||
|
||||
z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
|
||||
o = self.dec(z_slice, g=g)
|
||||
return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
|
||||
|
||||
def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
|
||||
x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
|
||||
if self.n_speakers > 0:
|
||||
g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
|
||||
else:
|
||||
g = None
|
||||
|
||||
if self.use_sdp:
|
||||
logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
|
||||
else:
|
||||
logw = self.dp(x, x_mask, g=g)
|
||||
w = torch.exp(logw) * x_mask * length_scale
|
||||
w_ceil = torch.ceil(w)
|
||||
y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
|
||||
y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
|
||||
attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
|
||||
attn = commons.generate_path(w_ceil, attn_mask)
|
||||
|
||||
m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
||||
logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
|
||||
|
||||
z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
|
||||
z = self.flow(z_p, y_mask, g=g, reverse=True)
|
||||
o = self.dec((z * y_mask)[:,:,:max_len], g=g)
|
||||
return o, attn, y_mask, (z, z_p, m_p, logs_p)
|
||||
|
||||
def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
|
||||
assert self.n_speakers > 0, "n_speakers have to be larger than 0."
|
||||
g_src = self.emb_g(sid_src).unsqueeze(-1)
|
||||
g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
|
||||
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
|
||||
z_p = self.flow(z, y_mask, g=g_src)
|
||||
z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
|
||||
o_hat = self.dec(z_hat * y_mask, g=g_tgt)
|
||||
return o_hat, y_mask, (z, z_p, z_hat)
|
||||
|
||||
390
models/modules.py
Normal file
390
models/modules.py
Normal file
@ -0,0 +1,390 @@
|
||||
import copy
|
||||
import math
|
||||
import numpy as np
|
||||
import scipy
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
import commons
|
||||
from commons import init_weights, get_padding
|
||||
from models.transforms import piecewise_rational_quadratic_transform
|
||||
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
assert n_layers > 1, "Number of layers should be larger than 0."
|
||||
|
||||
self.conv_layers = nn.ModuleList()
|
||||
self.norm_layers = nn.ModuleList()
|
||||
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers-1):
|
||||
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DDSConv(nn.Module):
|
||||
"""
|
||||
Dialted and Depth-Separable Convolution
|
||||
"""
|
||||
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.convs_sep = nn.ModuleList()
|
||||
self.convs_1x1 = nn.ModuleList()
|
||||
self.norms_1 = nn.ModuleList()
|
||||
self.norms_2 = nn.ModuleList()
|
||||
for i in range(n_layers):
|
||||
dilation = kernel_size ** i
|
||||
padding = (kernel_size * dilation - dilation) // 2
|
||||
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
||||
groups=channels, dilation=dilation, padding=padding
|
||||
))
|
||||
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
||||
self.norms_1.append(LayerNorm(channels))
|
||||
self.norms_2.append(LayerNorm(channels))
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
if g is not None:
|
||||
x = x + g
|
||||
for i in range(self.n_layers):
|
||||
y = self.convs_sep[i](x * x_mask)
|
||||
y = self.norms_1[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.convs_1x1[i](y)
|
||||
y = self.norms_2[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.drop(y)
|
||||
x = x + y
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
||||
super(WN, self).__init__()
|
||||
assert(kernel_size % 2 == 1)
|
||||
self.hidden_channels =hidden_channels
|
||||
self.kernel_size = kernel_size,
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate ** i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
||||
dilation=dilation, padding=padding)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = commons.fused_add_tanh_sigmoid_multiply(
|
||||
x_in,
|
||||
g_l,
|
||||
n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Log(nn.Module):
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
||||
logdet = torch.sum(-y, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = torch.exp(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ElementwiseAffine(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.m = nn.Parameter(torch.zeros(channels,1))
|
||||
self.logs = nn.Parameter(torch.zeros(channels,1))
|
||||
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = self.m + torch.exp(self.logs) * x
|
||||
y = y * x_mask
|
||||
logdet = torch.sum(self.logs * x_mask, [1,2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=0,
|
||||
gin_channels=0,
|
||||
mean_only=False):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1,2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
|
||||
|
||||
class ConvFlow(nn.Module):
|
||||
def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.num_bins = num_bins
|
||||
self.tail_bound = tail_bound
|
||||
self.half_channels = in_channels // 2
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
|
||||
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
|
||||
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0)
|
||||
h = self.convs(h, x_mask, g=g)
|
||||
h = self.proj(h) * x_mask
|
||||
|
||||
b, c, t = x0.shape
|
||||
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
|
||||
|
||||
unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
|
||||
unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
|
||||
unnormalized_derivatives = h[..., 2 * self.num_bins:]
|
||||
|
||||
x1, logabsdet = piecewise_rational_quadratic_transform(x1,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=reverse,
|
||||
tails='linear',
|
||||
tail_bound=self.tail_bound
|
||||
)
|
||||
|
||||
x = torch.cat([x0, x1], 1) * x_mask
|
||||
logdet = torch.sum(logabsdet * x_mask, [1,2])
|
||||
if not reverse:
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
193
models/transforms.py
Normal file
193
models/transforms.py
Normal file
@ -0,0 +1,193 @@
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
DEFAULT_MIN_BIN_WIDTH = 1e-3
|
||||
DEFAULT_MIN_BIN_HEIGHT = 1e-3
|
||||
DEFAULT_MIN_DERIVATIVE = 1e-3
|
||||
|
||||
|
||||
def piecewise_rational_quadratic_transform(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
tails=None,
|
||||
tail_bound=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
|
||||
if tails is None:
|
||||
spline_fn = rational_quadratic_spline
|
||||
spline_kwargs = {}
|
||||
else:
|
||||
spline_fn = unconstrained_rational_quadratic_spline
|
||||
spline_kwargs = {
|
||||
'tails': tails,
|
||||
'tail_bound': tail_bound
|
||||
}
|
||||
|
||||
outputs, logabsdet = spline_fn(
|
||||
inputs=inputs,
|
||||
unnormalized_widths=unnormalized_widths,
|
||||
unnormalized_heights=unnormalized_heights,
|
||||
unnormalized_derivatives=unnormalized_derivatives,
|
||||
inverse=inverse,
|
||||
min_bin_width=min_bin_width,
|
||||
min_bin_height=min_bin_height,
|
||||
min_derivative=min_derivative,
|
||||
**spline_kwargs
|
||||
)
|
||||
return outputs, logabsdet
|
||||
|
||||
|
||||
def searchsorted(bin_locations, inputs, eps=1e-6):
|
||||
bin_locations[..., -1] += eps
|
||||
return torch.sum(
|
||||
inputs[..., None] >= bin_locations,
|
||||
dim=-1
|
||||
) - 1
|
||||
|
||||
|
||||
def unconstrained_rational_quadratic_spline(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
tails='linear',
|
||||
tail_bound=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
|
||||
outside_interval_mask = ~inside_interval_mask
|
||||
|
||||
outputs = torch.zeros_like(inputs)
|
||||
logabsdet = torch.zeros_like(inputs)
|
||||
|
||||
if tails == 'linear':
|
||||
unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
|
||||
constant = np.log(np.exp(1 - min_derivative) - 1)
|
||||
unnormalized_derivatives[..., 0] = constant
|
||||
unnormalized_derivatives[..., -1] = constant
|
||||
|
||||
outputs[outside_interval_mask] = inputs[outside_interval_mask]
|
||||
logabsdet[outside_interval_mask] = 0
|
||||
else:
|
||||
raise RuntimeError('{} tails are not implemented.'.format(tails))
|
||||
|
||||
outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
|
||||
inputs=inputs[inside_interval_mask],
|
||||
unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
|
||||
unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
|
||||
unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
|
||||
inverse=inverse,
|
||||
left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
|
||||
min_bin_width=min_bin_width,
|
||||
min_bin_height=min_bin_height,
|
||||
min_derivative=min_derivative
|
||||
)
|
||||
|
||||
return outputs, logabsdet
|
||||
|
||||
def rational_quadratic_spline(inputs,
|
||||
unnormalized_widths,
|
||||
unnormalized_heights,
|
||||
unnormalized_derivatives,
|
||||
inverse=False,
|
||||
left=0., right=1., bottom=0., top=1.,
|
||||
min_bin_width=DEFAULT_MIN_BIN_WIDTH,
|
||||
min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
|
||||
min_derivative=DEFAULT_MIN_DERIVATIVE):
|
||||
if torch.min(inputs) < left or torch.max(inputs) > right:
|
||||
raise ValueError('Input to a transform is not within its domain')
|
||||
|
||||
num_bins = unnormalized_widths.shape[-1]
|
||||
|
||||
if min_bin_width * num_bins > 1.0:
|
||||
raise ValueError('Minimal bin width too large for the number of bins')
|
||||
if min_bin_height * num_bins > 1.0:
|
||||
raise ValueError('Minimal bin height too large for the number of bins')
|
||||
|
||||
widths = F.softmax(unnormalized_widths, dim=-1)
|
||||
widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
|
||||
cumwidths = torch.cumsum(widths, dim=-1)
|
||||
cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
|
||||
cumwidths = (right - left) * cumwidths + left
|
||||
cumwidths[..., 0] = left
|
||||
cumwidths[..., -1] = right
|
||||
widths = cumwidths[..., 1:] - cumwidths[..., :-1]
|
||||
|
||||
derivatives = min_derivative + F.softplus(unnormalized_derivatives)
|
||||
|
||||
heights = F.softmax(unnormalized_heights, dim=-1)
|
||||
heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
|
||||
cumheights = torch.cumsum(heights, dim=-1)
|
||||
cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
|
||||
cumheights = (top - bottom) * cumheights + bottom
|
||||
cumheights[..., 0] = bottom
|
||||
cumheights[..., -1] = top
|
||||
heights = cumheights[..., 1:] - cumheights[..., :-1]
|
||||
|
||||
if inverse:
|
||||
bin_idx = searchsorted(cumheights, inputs)[..., None]
|
||||
else:
|
||||
bin_idx = searchsorted(cumwidths, inputs)[..., None]
|
||||
|
||||
input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
|
||||
input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
|
||||
delta = heights / widths
|
||||
input_delta = delta.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
|
||||
input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
|
||||
|
||||
input_heights = heights.gather(-1, bin_idx)[..., 0]
|
||||
|
||||
if inverse:
|
||||
a = (((inputs - input_cumheights) * (input_derivatives
|
||||
+ input_derivatives_plus_one
|
||||
- 2 * input_delta)
|
||||
+ input_heights * (input_delta - input_derivatives)))
|
||||
b = (input_heights * input_derivatives
|
||||
- (inputs - input_cumheights) * (input_derivatives
|
||||
+ input_derivatives_plus_one
|
||||
- 2 * input_delta))
|
||||
c = - input_delta * (inputs - input_cumheights)
|
||||
|
||||
discriminant = b.pow(2) - 4 * a * c
|
||||
assert (discriminant >= 0).all()
|
||||
|
||||
root = (2 * c) / (-b - torch.sqrt(discriminant))
|
||||
outputs = root * input_bin_widths + input_cumwidths
|
||||
|
||||
theta_one_minus_theta = root * (1 - root)
|
||||
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
||||
* theta_one_minus_theta)
|
||||
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
|
||||
+ 2 * input_delta * theta_one_minus_theta
|
||||
+ input_derivatives * (1 - root).pow(2))
|
||||
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
||||
|
||||
return outputs, -logabsdet
|
||||
else:
|
||||
theta = (inputs - input_cumwidths) / input_bin_widths
|
||||
theta_one_minus_theta = theta * (1 - theta)
|
||||
|
||||
numerator = input_heights * (input_delta * theta.pow(2)
|
||||
+ input_derivatives * theta_one_minus_theta)
|
||||
denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
|
||||
* theta_one_minus_theta)
|
||||
outputs = input_cumheights + numerator / denominator
|
||||
|
||||
derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
|
||||
+ 2 * input_delta * theta_one_minus_theta
|
||||
+ input_derivatives * (1 - theta).pow(2))
|
||||
logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
|
||||
|
||||
return outputs, logabsdet
|
||||
258
models/utils.py
Normal file
258
models/utils.py
Normal file
@ -0,0 +1,258 @@
|
||||
import os
|
||||
import glob
|
||||
import sys
|
||||
import argparse
|
||||
import logging
|
||||
import json
|
||||
import subprocess
|
||||
import numpy as np
|
||||
from scipy.io.wavfile import read
|
||||
import torch
|
||||
|
||||
MATPLOTLIB_FLAG = False
|
||||
|
||||
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
||||
logger = logging
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
||||
assert os.path.isfile(checkpoint_path)
|
||||
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
||||
iteration = checkpoint_dict['iteration']
|
||||
learning_rate = checkpoint_dict['learning_rate']
|
||||
if optimizer is not None:
|
||||
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
||||
saved_state_dict = checkpoint_dict['model']
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
new_state_dict= {}
|
||||
for k, v in state_dict.items():
|
||||
try:
|
||||
new_state_dict[k] = saved_state_dict[k]
|
||||
except:
|
||||
logger.info("%s is not in the checkpoint" % k)
|
||||
new_state_dict[k] = v
|
||||
if hasattr(model, 'module'):
|
||||
model.module.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(new_state_dict)
|
||||
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
||||
checkpoint_path, iteration))
|
||||
return model, optimizer, learning_rate, iteration
|
||||
|
||||
|
||||
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
||||
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
||||
iteration, checkpoint_path))
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
torch.save({'model': state_dict,
|
||||
'iteration': iteration,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'learning_rate': learning_rate}, checkpoint_path)
|
||||
|
||||
|
||||
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
||||
for k, v in scalars.items():
|
||||
writer.add_scalar(k, v, global_step)
|
||||
for k, v in histograms.items():
|
||||
writer.add_histogram(k, v, global_step)
|
||||
for k, v in images.items():
|
||||
writer.add_image(k, v, global_step, dataformats='HWC')
|
||||
for k, v in audios.items():
|
||||
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
||||
|
||||
|
||||
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
||||
f_list = glob.glob(os.path.join(dir_path, regex))
|
||||
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
||||
x = f_list[-1]
|
||||
print(x)
|
||||
return x
|
||||
|
||||
|
||||
def plot_spectrogram_to_numpy(spectrogram):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10,2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
plt.xlabel("Frames")
|
||||
plt.ylabel("Channels")
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def plot_alignment_to_numpy(alignment, info=None):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
||||
interpolation='none')
|
||||
fig.colorbar(im, ax=ax)
|
||||
xlabel = 'Decoder timestep'
|
||||
if info is not None:
|
||||
xlabel += '\n\n' + info
|
||||
plt.xlabel(xlabel)
|
||||
plt.ylabel('Encoder timestep')
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def load_wav_to_torch(full_path):
|
||||
sampling_rate, data = read(full_path)
|
||||
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
||||
|
||||
|
||||
def load_filepaths_and_text(filename, split="|"):
|
||||
with open(filename, encoding='utf-8') as f:
|
||||
filepaths_and_text = [line.strip().split(split) for line in f]
|
||||
return filepaths_and_text
|
||||
|
||||
|
||||
def get_hparams(init=True):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
||||
help='JSON file for configuration')
|
||||
parser.add_argument('-m', '--model', type=str, required=True,
|
||||
help='Model name')
|
||||
|
||||
args = parser.parse_args()
|
||||
model_dir = os.path.join("./logs", args.model)
|
||||
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
|
||||
config_path = args.config
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
if init:
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
with open(config_save_path, "w") as f:
|
||||
f.write(data)
|
||||
else:
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_dir(model_dir):
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_file(config_path):
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
return hparams
|
||||
|
||||
|
||||
def check_git_hash(model_dir):
|
||||
source_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
if not os.path.exists(os.path.join(source_dir, ".git")):
|
||||
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
||||
source_dir
|
||||
))
|
||||
return
|
||||
|
||||
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
||||
|
||||
path = os.path.join(model_dir, "githash")
|
||||
if os.path.exists(path):
|
||||
saved_hash = open(path).read()
|
||||
if saved_hash != cur_hash:
|
||||
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
||||
saved_hash[:8], cur_hash[:8]))
|
||||
else:
|
||||
open(path, "w").write(cur_hash)
|
||||
|
||||
|
||||
def get_logger(model_dir, filename="train.log"):
|
||||
global logger
|
||||
logger = logging.getLogger(os.path.basename(model_dir))
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
h = logging.FileHandler(os.path.join(model_dir, filename))
|
||||
h.setLevel(logging.DEBUG)
|
||||
h.setFormatter(formatter)
|
||||
logger.addHandler(h)
|
||||
return logger
|
||||
|
||||
|
||||
class HParams():
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
19
monotonic_align/__init__.py
Normal file
19
monotonic_align/__init__.py
Normal file
@ -0,0 +1,19 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from .monotonic_align.core import maximum_path_c
|
||||
|
||||
|
||||
def maximum_path(neg_cent, mask):
|
||||
""" Cython optimized version.
|
||||
neg_cent: [b, t_t, t_s]
|
||||
mask: [b, t_t, t_s]
|
||||
"""
|
||||
device = neg_cent.device
|
||||
dtype = neg_cent.dtype
|
||||
neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
|
||||
path = np.zeros(neg_cent.shape, dtype=np.int32)
|
||||
|
||||
t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
|
||||
t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
|
||||
maximum_path_c(path, neg_cent, t_t_max, t_s_max)
|
||||
return torch.from_numpy(path).to(device=device, dtype=dtype)
|
||||
BIN
monotonic_align/__pycache__/__init__.cpython-312.pyc
Normal file
BIN
monotonic_align/__pycache__/__init__.cpython-312.pyc
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
BIN
monotonic_align/build/temp.linux-x86_64-cpython-312/core.o
Normal file
BIN
monotonic_align/build/temp.linux-x86_64-cpython-312/core.o
Normal file
Binary file not shown.
BIN
monotonic_align/build/temp.macosx-15.1-arm64-cpython-312/core.o
Normal file
BIN
monotonic_align/build/temp.macosx-15.1-arm64-cpython-312/core.o
Normal file
Binary file not shown.
27528
monotonic_align/core.c
Normal file
27528
monotonic_align/core.c
Normal file
File diff suppressed because it is too large
Load Diff
BIN
monotonic_align/core.cpython-312-x86_64-linux-gnu.so
Normal file
BIN
monotonic_align/core.cpython-312-x86_64-linux-gnu.so
Normal file
Binary file not shown.
42
monotonic_align/core.pyx
Normal file
42
monotonic_align/core.pyx
Normal file
@ -0,0 +1,42 @@
|
||||
cimport cython
|
||||
from cython.parallel import prange
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
|
||||
cdef int x
|
||||
cdef int y
|
||||
cdef float v_prev
|
||||
cdef float v_cur
|
||||
cdef float tmp
|
||||
cdef int index = t_x - 1
|
||||
|
||||
for y in range(t_y):
|
||||
for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
|
||||
if x == y:
|
||||
v_cur = max_neg_val
|
||||
else:
|
||||
v_cur = value[y-1, x]
|
||||
if x == 0:
|
||||
if y == 0:
|
||||
v_prev = 0.
|
||||
else:
|
||||
v_prev = max_neg_val
|
||||
else:
|
||||
v_prev = value[y-1, x-1]
|
||||
value[y, x] += max(v_prev, v_cur)
|
||||
|
||||
for y in range(t_y - 1, -1, -1):
|
||||
path[y, index] = 1
|
||||
if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
|
||||
index = index - 1
|
||||
|
||||
|
||||
@cython.boundscheck(False)
|
||||
@cython.wraparound(False)
|
||||
cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
|
||||
cdef int b = paths.shape[0]
|
||||
cdef int i
|
||||
for i in prange(b, nogil=True):
|
||||
maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
|
||||
BIN
monotonic_align/monotonic_align/core.cpython-312-darwin.so
Normal file
BIN
monotonic_align/monotonic_align/core.cpython-312-darwin.so
Normal file
Binary file not shown.
Binary file not shown.
9
monotonic_align/setup.py
Normal file
9
monotonic_align/setup.py
Normal file
@ -0,0 +1,9 @@
|
||||
from distutils.core import setup
|
||||
from Cython.Build import cythonize
|
||||
import numpy
|
||||
|
||||
setup(
|
||||
name = 'monotonic_align',
|
||||
ext_modules = cythonize("core.pyx"),
|
||||
include_dirs=[numpy.get_include()]
|
||||
)
|
||||
37
requirements.txt
Normal file
37
requirements.txt
Normal file
@ -0,0 +1,37 @@
|
||||
blinker==1.9.0
|
||||
cffi==1.17.1
|
||||
click==8.1.7
|
||||
Cython==3.0.11
|
||||
filelock==3.16.1
|
||||
Flask==3.1.0
|
||||
fsspec==2024.10.0
|
||||
itsdangerous==2.2.0
|
||||
Jinja2==3.1.4
|
||||
MarkupSafe==3.0.2
|
||||
mpmath==1.3.0
|
||||
networkx==3.4.2
|
||||
numpy==2.1.3
|
||||
nvidia-cublas-cu12==12.4.5.8
|
||||
nvidia-cuda-cupti-cu12==12.4.127
|
||||
nvidia-cuda-nvrtc-cu12==12.4.127
|
||||
nvidia-cuda-runtime-cu12==12.4.127
|
||||
nvidia-cudnn-cu12==9.1.0.70
|
||||
nvidia-cufft-cu12==11.2.1.3
|
||||
nvidia-curand-cu12==10.3.5.147
|
||||
nvidia-cusolver-cu12==11.6.1.9
|
||||
nvidia-cusparse-cu12==12.3.1.170
|
||||
nvidia-nccl-cu12==2.21.5
|
||||
nvidia-nvjitlink-cu12==12.4.127
|
||||
nvidia-nvtx-cu12==12.4.127
|
||||
pycparser==2.22
|
||||
pyopenjtalk==0.3.4
|
||||
scipy==1.14.1
|
||||
setuptools==75.5.0
|
||||
soundfile==0.12.1
|
||||
sympy==1.13.1
|
||||
torch==2.5.1
|
||||
tqdm==4.67.0
|
||||
triton==3.1.0
|
||||
typing_extensions==4.12.2
|
||||
Unidecode==1.3.8
|
||||
Werkzeug==3.1.3
|
||||
19
text/LICENSE
Normal file
19
text/LICENSE
Normal file
@ -0,0 +1,19 @@
|
||||
Copyright (c) 2017 Keith Ito
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in
|
||||
all copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
|
||||
THE SOFTWARE.
|
||||
55
text/__init__.py
Normal file
55
text/__init__.py
Normal file
@ -0,0 +1,55 @@
|
||||
""" from https://github.com/keithito/tacotron """
|
||||
from text import cleaners
|
||||
from text.symbols import symbols
|
||||
|
||||
|
||||
# Mappings from symbol to numeric ID and vice versa:
|
||||
_symbol_to_id = {s: i for i, s in enumerate(symbols)}
|
||||
_id_to_symbol = {i: s for i, s in enumerate(symbols)}
|
||||
|
||||
|
||||
def text_to_sequence(text, cleaner_names):
|
||||
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
||||
Args:
|
||||
text: string to convert to a sequence
|
||||
cleaner_names: names of the cleaner functions to run the text through
|
||||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
'''
|
||||
sequence = []
|
||||
text = text.replace('\n', ' ')
|
||||
text = text.replace('|', ' ')
|
||||
clean_text = _clean_text(text, cleaner_names)
|
||||
for symbol in clean_text:
|
||||
symbol_id = _symbol_to_id[symbol]
|
||||
sequence += [symbol_id]
|
||||
return sequence
|
||||
|
||||
|
||||
def cleaned_text_to_sequence(cleaned_text):
|
||||
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
|
||||
Args:
|
||||
text: string to convert to a sequence
|
||||
Returns:
|
||||
List of integers corresponding to the symbols in the text
|
||||
'''
|
||||
sequence = [_symbol_to_id[symbol] for symbol in cleaned_text]
|
||||
return sequence
|
||||
|
||||
|
||||
def sequence_to_text(sequence):
|
||||
'''Converts a sequence of IDs back to a string'''
|
||||
result = ''
|
||||
for symbol_id in sequence:
|
||||
s = _id_to_symbol[symbol_id]
|
||||
result += s
|
||||
return result
|
||||
|
||||
|
||||
def _clean_text(text, cleaner_names):
|
||||
for name in cleaner_names:
|
||||
cleaner = getattr(cleaners, name)
|
||||
if not cleaner:
|
||||
raise Exception('Unknown cleaner: %s' % name)
|
||||
text = cleaner(text)
|
||||
return text
|
||||
BIN
text/__pycache__/__init__.cpython-312.pyc
Normal file
BIN
text/__pycache__/__init__.cpython-312.pyc
Normal file
Binary file not shown.
BIN
text/__pycache__/cleaners.cpython-312.pyc
Normal file
BIN
text/__pycache__/cleaners.cpython-312.pyc
Normal file
Binary file not shown.
BIN
text/__pycache__/symbols.cpython-312.pyc
Normal file
BIN
text/__pycache__/symbols.cpython-312.pyc
Normal file
Binary file not shown.
25
text/cleaners.py
Normal file
25
text/cleaners.py
Normal file
@ -0,0 +1,25 @@
|
||||
import re
|
||||
from unidecode import unidecode
|
||||
import pyopenjtalk
|
||||
|
||||
# Regular expression matching Japanese without punctuation marks:
|
||||
_japanese_characters = re.compile(r'[A-Za-z\d々-ヿ一-鿿１-９Ａ-Ｚａ-ｚｦ-ﾝ]')
|
||||
|
||||
# Regular expression matching non-Japanese characters or punctuation marks:
|
||||
_japanese_marks = re.compile(r'[^A-Za-z\d々-ヿ一-鿿１-９Ａ-Ｚａ-ｚｦ-ﾝ]')
|
||||
|
||||
# List of (regular expression, replacement) pairs for abbreviations:
|
||||
|
||||
def japanese_cleaners(text):
|
||||
sentences = re.split(_japanese_marks, text)
|
||||
marks = re.findall(_japanese_marks, text)
|
||||
text = ''
|
||||
for i, mark in enumerate(marks):
|
||||
if re.match(_japanese_characters, sentences[i]):
|
||||
text += pyopenjtalk.g2p(sentences[i], kana=False).replace('pau','').replace(' ','')
|
||||
text += unidecode(mark).replace(' ','')
|
||||
if re.match(_japanese_characters, sentences[-1]):
|
||||
text += pyopenjtalk.g2p(sentences[-1], kana=False).replace('pau','').replace(' ','')
|
||||
if re.match('[A-Za-z]',text[-1]):
|
||||
text += '.'
|
||||
return text
|
||||
11
text/symbols.py
Normal file
11
text/symbols.py
Normal file
@ -0,0 +1,11 @@
|
||||
|
||||
_pad = '_'
|
||||
_punctuation = """!"'(),.:;?{}<>\^[]/+- """
|
||||
_special = '-~%#@&*$'
|
||||
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz1234567890'
|
||||
|
||||
# I trained with wrong tokens... these thing is for that.
|
||||
_dummy = ["=" for i in range(84)]
|
||||
|
||||
# Export all symbols:
|
||||
symbols = [_pad] + list(_special) + list(_punctuation) + list(_letters) + _dummy
|
||||
Loading…
x
Reference in New Issue
Block a user