495 lines
16 KiB
Python
495 lines
16 KiB
Python
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import argparse
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import contextlib
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import sys
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from argparse import RawTextHelpFormatter
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# pylint: disable=redefined-outer-name, unused-argument
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from pathlib import Path
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description = """
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Synthesize speech on command line.
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You can either use your trained model or choose a model from the provided list.
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If you don't specify any models, then it uses LJSpeech based English model.
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#### Single Speaker Models
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- List provided models:
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```
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$ tts --list_models
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```
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- Get model info (for both tts_models and vocoder_models):
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- Query by type/name:
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The model_info_by_name uses the name as it from the --list_models.
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```
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$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
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```
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For example:
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```
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$ tts --model_info_by_name tts_models/tr/common-voice/glow-tts
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$ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
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```
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- Query by type/idx:
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The model_query_idx uses the corresponding idx from --list_models.
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```
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$ tts --model_info_by_idx "<model_type>/<model_query_idx>"
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```
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For example:
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```
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$ tts --model_info_by_idx tts_models/3
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```
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- Query info for model info by full name:
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```
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$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
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```
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- Run TTS with default models:
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```
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$ tts --text "Text for TTS" --out_path output/path/speech.wav
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```
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- Run TTS and pipe out the generated TTS wav file data:
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```
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$ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay
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```
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- Run a TTS model with its default vocoder model:
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```
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$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
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```
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For example:
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```
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$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav
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```
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- Run with specific TTS and vocoder models from the list:
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```
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$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
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```
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For example:
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```
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$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --vocoder_name "vocoder_models/en/ljspeech/univnet" --out_path output/path/speech.wav
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```
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- Run your own TTS model (Using Griffin-Lim Vocoder):
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```
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$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
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```
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- Run your own TTS and Vocoder models:
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```
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$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
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--vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json
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```
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#### Multi-speaker Models
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- List the available speakers and choose a <speaker_id> among them:
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```
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$ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs
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```
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- Run the multi-speaker TTS model with the target speaker ID:
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```
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$ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id>
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```
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- Run your own multi-speaker TTS model:
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```
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$ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
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```
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### Voice Conversion Models
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```
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$ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --source_wav <path/to/speaker/wav> --target_wav <path/to/reference/wav>
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```
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"""
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def str2bool(v):
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if isinstance(v, bool):
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return v
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if v.lower() in ("yes", "true", "t", "y", "1"):
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return True
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if v.lower() in ("no", "false", "f", "n", "0"):
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return False
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raise argparse.ArgumentTypeError("Boolean value expected.")
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def main():
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parser = argparse.ArgumentParser(
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description=description.replace(" ```\n", ""),
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formatter_class=RawTextHelpFormatter,
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)
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parser.add_argument(
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"--list_models",
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type=str2bool,
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nargs="?",
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const=True,
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default=False,
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help="list available pre-trained TTS and vocoder models.",
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)
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parser.add_argument(
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"--model_info_by_idx",
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type=str,
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default=None,
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help="model info using query format: <model_type>/<model_query_idx>",
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)
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parser.add_argument(
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"--model_info_by_name",
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type=str,
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default=None,
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help="model info using query format: <model_type>/<language>/<dataset>/<model_name>",
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)
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parser.add_argument("--text", type=str, default=None, help="Text to generate speech.")
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# Args for running pre-trained TTS models.
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parser.add_argument(
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"--model_name",
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type=str,
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default="tts_models/en/ljspeech/tacotron2-DDC",
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help="Name of one of the pre-trained TTS models in format <language>/<dataset>/<model_name>",
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)
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parser.add_argument(
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"--vocoder_name",
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type=str,
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default=None,
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help="Name of one of the pre-trained vocoder models in format <language>/<dataset>/<model_name>",
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)
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# Args for running custom models
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parser.add_argument("--config_path", default=None, type=str, help="Path to model config file.")
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parser.add_argument(
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"--model_path",
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type=str,
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default=None,
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help="Path to model file.",
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)
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parser.add_argument(
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"--out_path",
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type=str,
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default="tts_output.wav",
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help="Output wav file path.",
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)
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parser.add_argument("--use_cuda", type=bool, help="Run model on CUDA.", default=False)
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parser.add_argument("--device", type=str, help="Device to run model on.", default="cpu")
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parser.add_argument(
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"--vocoder_path",
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type=str,
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help="Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).",
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default=None,
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)
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parser.add_argument("--vocoder_config_path", type=str, help="Path to vocoder model config file.", default=None)
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parser.add_argument(
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"--encoder_path",
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type=str,
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help="Path to speaker encoder model file.",
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default=None,
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)
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parser.add_argument("--encoder_config_path", type=str, help="Path to speaker encoder config file.", default=None)
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parser.add_argument(
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"--pipe_out",
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help="stdout the generated TTS wav file for shell pipe.",
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type=str2bool,
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nargs="?",
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const=True,
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default=False,
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)
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# args for multi-speaker synthesis
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parser.add_argument("--speakers_file_path", type=str, help="JSON file for multi-speaker model.", default=None)
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parser.add_argument("--language_ids_file_path", type=str, help="JSON file for multi-lingual model.", default=None)
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parser.add_argument(
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"--speaker_idx",
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type=str,
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help="Target speaker ID for a multi-speaker TTS model.",
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default=None,
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)
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parser.add_argument(
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"--language_idx",
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type=str,
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help="Target language ID for a multi-lingual TTS model.",
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default=None,
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)
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parser.add_argument(
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"--speaker_wav",
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nargs="+",
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help="wav file(s) to condition a multi-speaker TTS model with a Speaker Encoder. You can give multiple file paths. The d_vectors is computed as their average.",
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default=None,
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)
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parser.add_argument("--gst_style", help="Wav path file for GST style reference.", default=None)
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parser.add_argument(
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"--capacitron_style_wav", type=str, help="Wav path file for Capacitron prosody reference.", default=None
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)
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parser.add_argument("--capacitron_style_text", type=str, help="Transcription of the reference.", default=None)
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parser.add_argument(
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"--list_speaker_idxs",
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help="List available speaker ids for the defined multi-speaker model.",
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type=str2bool,
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nargs="?",
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const=True,
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default=False,
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)
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parser.add_argument(
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"--list_language_idxs",
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help="List available language ids for the defined multi-lingual model.",
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type=str2bool,
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nargs="?",
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const=True,
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default=False,
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)
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# aux args
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parser.add_argument(
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"--save_spectogram",
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type=bool,
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help="If true save raw spectogram for further (vocoder) processing in out_path.",
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default=False,
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)
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parser.add_argument(
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"--reference_wav",
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type=str,
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help="Reference wav file to convert in the voice of the speaker_idx or speaker_wav",
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default=None,
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)
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parser.add_argument(
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"--reference_speaker_idx",
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type=str,
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help="speaker ID of the reference_wav speaker (If not provided the embedding will be computed using the Speaker Encoder).",
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default=None,
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)
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parser.add_argument(
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"--progress_bar",
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type=str2bool,
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help="If true shows a progress bar for the model download. Defaults to True",
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default=True,
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)
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# voice conversion args
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parser.add_argument(
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"--source_wav",
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type=str,
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default=None,
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help="Original audio file to convert in the voice of the target_wav",
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)
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parser.add_argument(
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"--target_wav",
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type=str,
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default=None,
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help="Target audio file to convert in the voice of the source_wav",
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)
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parser.add_argument(
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"--voice_dir",
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type=str,
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default=None,
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help="Voice dir for tortoise model",
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)
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args = parser.parse_args()
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# print the description if either text or list_models is not set
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check_args = [
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args.text,
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args.list_models,
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args.list_speaker_idxs,
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args.list_language_idxs,
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args.reference_wav,
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args.model_info_by_idx,
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args.model_info_by_name,
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args.source_wav,
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args.target_wav,
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]
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if not any(check_args):
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parser.parse_args(["-h"])
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pipe_out = sys.stdout if args.pipe_out else None
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with contextlib.redirect_stdout(None if args.pipe_out else sys.stdout):
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# Late-import to make things load faster
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from TTS.api import TTS
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from TTS.utils.manage import ModelManager
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from TTS.utils.synthesizer import Synthesizer
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# load model manager
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path = Path(__file__).parent / "../.models.json"
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manager = ModelManager(path, progress_bar=args.progress_bar)
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api = TTS()
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tts_path = None
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tts_config_path = None
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speakers_file_path = None
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language_ids_file_path = None
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vocoder_path = None
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vocoder_config_path = None
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encoder_path = None
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encoder_config_path = None
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vc_path = None
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vc_config_path = None
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model_dir = None
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# CASE1 #list : list pre-trained TTS models
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if args.list_models:
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manager.list_models()
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sys.exit()
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# CASE2 #info : model info for pre-trained TTS models
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if args.model_info_by_idx:
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model_query = args.model_info_by_idx
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manager.model_info_by_idx(model_query)
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sys.exit()
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if args.model_info_by_name:
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model_query_full_name = args.model_info_by_name
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manager.model_info_by_full_name(model_query_full_name)
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sys.exit()
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# CASE3: load pre-trained model paths
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if args.model_name is not None and not args.model_path:
|
||
|
|
model_path, config_path, model_item = manager.download_model(args.model_name)
|
||
|
|
# tts model
|
||
|
|
if model_item["model_type"] == "tts_models":
|
||
|
|
tts_path = model_path
|
||
|
|
tts_config_path = config_path
|
||
|
|
if "default_vocoder" in model_item:
|
||
|
|
args.vocoder_name = (
|
||
|
|
model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
|
||
|
|
)
|
||
|
|
|
||
|
|
# voice conversion model
|
||
|
|
if model_item["model_type"] == "voice_conversion_models":
|
||
|
|
vc_path = model_path
|
||
|
|
vc_config_path = config_path
|
||
|
|
|
||
|
|
# tts model with multiple files to be loaded from the directory path
|
||
|
|
if model_item.get("author", None) == "fairseq" or isinstance(model_item["model_url"], list):
|
||
|
|
model_dir = model_path
|
||
|
|
tts_path = None
|
||
|
|
tts_config_path = None
|
||
|
|
args.vocoder_name = None
|
||
|
|
|
||
|
|
# load vocoder
|
||
|
|
if args.vocoder_name is not None and not args.vocoder_path:
|
||
|
|
vocoder_path, vocoder_config_path, _ = manager.download_model(args.vocoder_name)
|
||
|
|
|
||
|
|
# CASE4: set custom model paths
|
||
|
|
if args.model_path is not None:
|
||
|
|
tts_path = args.model_path
|
||
|
|
tts_config_path = args.config_path
|
||
|
|
speakers_file_path = args.speakers_file_path
|
||
|
|
language_ids_file_path = args.language_ids_file_path
|
||
|
|
|
||
|
|
if args.vocoder_path is not None:
|
||
|
|
vocoder_path = args.vocoder_path
|
||
|
|
vocoder_config_path = args.vocoder_config_path
|
||
|
|
|
||
|
|
if args.encoder_path is not None:
|
||
|
|
encoder_path = args.encoder_path
|
||
|
|
encoder_config_path = args.encoder_config_path
|
||
|
|
|
||
|
|
device = args.device
|
||
|
|
if args.use_cuda:
|
||
|
|
device = "cuda"
|
||
|
|
|
||
|
|
# load models
|
||
|
|
synthesizer = Synthesizer(
|
||
|
|
tts_path,
|
||
|
|
tts_config_path,
|
||
|
|
speakers_file_path,
|
||
|
|
language_ids_file_path,
|
||
|
|
vocoder_path,
|
||
|
|
vocoder_config_path,
|
||
|
|
encoder_path,
|
||
|
|
encoder_config_path,
|
||
|
|
vc_path,
|
||
|
|
vc_config_path,
|
||
|
|
model_dir,
|
||
|
|
args.voice_dir,
|
||
|
|
).to(device)
|
||
|
|
|
||
|
|
# query speaker ids of a multi-speaker model.
|
||
|
|
if args.list_speaker_idxs:
|
||
|
|
print(
|
||
|
|
" > Available speaker ids: (Set --speaker_idx flag to one of these values to use the multi-speaker model."
|
||
|
|
)
|
||
|
|
print(synthesizer.tts_model.speaker_manager.name_to_id)
|
||
|
|
return
|
||
|
|
|
||
|
|
# query langauge ids of a multi-lingual model.
|
||
|
|
if args.list_language_idxs:
|
||
|
|
print(
|
||
|
|
" > Available language ids: (Set --language_idx flag to one of these values to use the multi-lingual model."
|
||
|
|
)
|
||
|
|
print(synthesizer.tts_model.language_manager.name_to_id)
|
||
|
|
return
|
||
|
|
|
||
|
|
# check the arguments against a multi-speaker model.
|
||
|
|
if synthesizer.tts_speakers_file and (not args.speaker_idx and not args.speaker_wav):
|
||
|
|
print(
|
||
|
|
" [!] Looks like you use a multi-speaker model. Define `--speaker_idx` to "
|
||
|
|
"select the target speaker. You can list the available speakers for this model by `--list_speaker_idxs`."
|
||
|
|
)
|
||
|
|
return
|
||
|
|
|
||
|
|
# RUN THE SYNTHESIS
|
||
|
|
if args.text:
|
||
|
|
print(" > Text: {}".format(args.text))
|
||
|
|
|
||
|
|
# kick it
|
||
|
|
if tts_path is not None:
|
||
|
|
wav = synthesizer.tts(
|
||
|
|
args.text,
|
||
|
|
speaker_name=args.speaker_idx,
|
||
|
|
language_name=args.language_idx,
|
||
|
|
speaker_wav=args.speaker_wav,
|
||
|
|
reference_wav=args.reference_wav,
|
||
|
|
style_wav=args.capacitron_style_wav,
|
||
|
|
style_text=args.capacitron_style_text,
|
||
|
|
reference_speaker_name=args.reference_speaker_idx,
|
||
|
|
)
|
||
|
|
elif vc_path is not None:
|
||
|
|
wav = synthesizer.voice_conversion(
|
||
|
|
source_wav=args.source_wav,
|
||
|
|
target_wav=args.target_wav,
|
||
|
|
)
|
||
|
|
elif model_dir is not None:
|
||
|
|
wav = synthesizer.tts(
|
||
|
|
args.text, speaker_name=args.speaker_idx, language_name=args.language_idx, speaker_wav=args.speaker_wav
|
||
|
|
)
|
||
|
|
|
||
|
|
# save the results
|
||
|
|
print(" > Saving output to {}".format(args.out_path))
|
||
|
|
synthesizer.save_wav(wav, args.out_path, pipe_out=pipe_out)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|