Voice-Clone2 / TTS /bin /synthesize.py
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voice-clone with single audio sample input
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import contextlib
import sys
from argparse import RawTextHelpFormatter
# pylint: disable=redefined-outer-name, unused-argument
from pathlib import Path
description = """
Synthesize speech on command line.
You can either use your trained model or choose a model from the provided list.
If you don't specify any models, then it uses LJSpeech based English model.
#### Single Speaker Models
- List provided models:
```
$ tts --list_models
```
- Get model info (for both tts_models and vocoder_models):
- Query by type/name:
The model_info_by_name uses the name as it from the --list_models.
```
$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
```
For example:
```
$ tts --model_info_by_name tts_models/tr/common-voice/glow-tts
$ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
```
- Query by type/idx:
The model_query_idx uses the corresponding idx from --list_models.
```
$ tts --model_info_by_idx "<model_type>/<model_query_idx>"
```
For example:
```
$ tts --model_info_by_idx tts_models/3
```
- Query info for model info by full name:
```
$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
```
- Run TTS with default models:
```
$ tts --text "Text for TTS" --out_path output/path/speech.wav
```
- Run TTS and pipe out the generated TTS wav file data:
```
$ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay
```
- Run TTS and define speed factor to use for 🐸Coqui Studio models, between 0.0 and 2.0:
```
$ tts --text "Text for TTS" --model_name "coqui_studio/<language>/<dataset>/<model_name>" --speed 1.2 --out_path output/path/speech.wav
```
- Run a TTS model with its default vocoder model:
```
$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
```
For example:
```
$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav
```
- Run with specific TTS and vocoder models from the list:
```
$ 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
```
For example:
```
$ 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
```
- Run your own TTS model (Using Griffin-Lim Vocoder):
```
$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
```
- Run your own TTS and Vocoder models:
```
$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json
```
#### Multi-speaker Models
- List the available speakers and choose a <speaker_id> among them:
```
$ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs
```
- Run the multi-speaker TTS model with the target speaker ID:
```
$ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id>
```
- Run your own multi-speaker TTS model:
```
$ 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>
```
### Voice Conversion Models
```
$ 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>
```
"""
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
if v.lower() in ("no", "false", "f", "n", "0"):
return False
raise argparse.ArgumentTypeError("Boolean value expected.")
def main():
parser = argparse.ArgumentParser(
description=description.replace(" ```\n", ""),
formatter_class=RawTextHelpFormatter,
)
parser.add_argument(
"--list_models",
type=str2bool,
nargs="?",
const=True,
default=False,
help="list available pre-trained TTS and vocoder models.",
)
parser.add_argument(
"--model_info_by_idx",
type=str,
default=None,
help="model info using query format: <model_type>/<model_query_idx>",
)
parser.add_argument(
"--model_info_by_name",
type=str,
default=None,
help="model info using query format: <model_type>/<language>/<dataset>/<model_name>",
)
parser.add_argument("--text", type=str, default=None, help="Text to generate speech.")
# Args for running pre-trained TTS models.
parser.add_argument(
"--model_name",
type=str,
default="tts_models/en/ljspeech/tacotron2-DDC",
help="Name of one of the pre-trained TTS models in format <language>/<dataset>/<model_name>",
)
parser.add_argument(
"--vocoder_name",
type=str,
default=None,
help="Name of one of the pre-trained vocoder models in format <language>/<dataset>/<model_name>",
)
# Args for running custom models
parser.add_argument("--config_path", default=None, type=str, help="Path to model config file.")
parser.add_argument(
"--model_path",
type=str,
default=None,
help="Path to model file.",
)
parser.add_argument(
"--out_path",
type=str,
default="tts_output.wav",
help="Output wav file path.",
)
parser.add_argument("--use_cuda", type=bool, help="Run model on CUDA.", default=False)
parser.add_argument("--device", type=str, help="Device to run model on.", default="cpu")
parser.add_argument(
"--vocoder_path",
type=str,
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).",
default=None,
)
parser.add_argument("--vocoder_config_path", type=str, help="Path to vocoder model config file.", default=None)
parser.add_argument(
"--encoder_path",
type=str,
help="Path to speaker encoder model file.",
default=None,
)
parser.add_argument("--encoder_config_path", type=str, help="Path to speaker encoder config file.", default=None)
# args for coqui studio
parser.add_argument(
"--cs_model",
type=str,
help="Name of the 🐸Coqui Studio model. Available models are `XTTS`, `V1`.",
)
parser.add_argument(
"--emotion",
type=str,
help="Emotion to condition the model with. Only available for 🐸Coqui Studio `V1` model.",
default=None,
)
parser.add_argument(
"--language",
type=str,
help="Language to condition the model with. Only available for 🐸Coqui Studio `XTTS` model.",
default=None,
)
parser.add_argument(
"--pipe_out",
help="stdout the generated TTS wav file for shell pipe.",
type=str2bool,
nargs="?",
const=True,
default=False,
)
parser.add_argument(
"--speed",
type=float,
help="Speed factor to use for 🐸Coqui Studio models, between 0.0 and 2.0.",
default=None,
)
# args for multi-speaker synthesis
parser.add_argument("--speakers_file_path", type=str, help="JSON file for multi-speaker model.", default=None)
parser.add_argument("--language_ids_file_path", type=str, help="JSON file for multi-lingual model.", default=None)
parser.add_argument(
"--speaker_idx",
type=str,
help="Target speaker ID for a multi-speaker TTS model.",
default=None,
)
parser.add_argument(
"--language_idx",
type=str,
help="Target language ID for a multi-lingual TTS model.",
default=None,
)
parser.add_argument(
"--speaker_wav",
nargs="+",
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.",
default=None,
)
parser.add_argument("--gst_style", help="Wav path file for GST style reference.", default=None)
parser.add_argument(
"--capacitron_style_wav", type=str, help="Wav path file for Capacitron prosody reference.", default=None
)
parser.add_argument("--capacitron_style_text", type=str, help="Transcription of the reference.", default=None)
parser.add_argument(
"--list_speaker_idxs",
help="List available speaker ids for the defined multi-speaker model.",
type=str2bool,
nargs="?",
const=True,
default=False,
)
parser.add_argument(
"--list_language_idxs",
help="List available language ids for the defined multi-lingual model.",
type=str2bool,
nargs="?",
const=True,
default=False,
)
# aux args
parser.add_argument(
"--save_spectogram",
type=bool,
help="If true save raw spectogram for further (vocoder) processing in out_path.",
default=False,
)
parser.add_argument(
"--reference_wav",
type=str,
help="Reference wav file to convert in the voice of the speaker_idx or speaker_wav",
default=None,
)
parser.add_argument(
"--reference_speaker_idx",
type=str,
help="speaker ID of the reference_wav speaker (If not provided the embedding will be computed using the Speaker Encoder).",
default=None,
)
parser.add_argument(
"--progress_bar",
type=str2bool,
help="If true shows a progress bar for the model download. Defaults to True",
default=True,
)
# voice conversion args
parser.add_argument(
"--source_wav",
type=str,
default=None,
help="Original audio file to convert in the voice of the target_wav",
)
parser.add_argument(
"--target_wav",
type=str,
default=None,
help="Target audio file to convert in the voice of the source_wav",
)
parser.add_argument(
"--voice_dir",
type=str,
default=None,
help="Voice dir for tortoise model",
)
args = parser.parse_args()
# print the description if either text or list_models is not set
check_args = [
args.text,
args.list_models,
args.list_speaker_idxs,
args.list_language_idxs,
args.reference_wav,
args.model_info_by_idx,
args.model_info_by_name,
args.source_wav,
args.target_wav,
]
if not any(check_args):
parser.parse_args(["-h"])
pipe_out = sys.stdout if args.pipe_out else None
with contextlib.redirect_stdout(None if args.pipe_out else sys.stdout):
# Late-import to make things load faster
from TTS.api import TTS
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
# load model manager
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path, progress_bar=args.progress_bar)
api = TTS()
tts_path = None
tts_config_path = None
speakers_file_path = None
language_ids_file_path = None
vocoder_path = None
vocoder_config_path = None
encoder_path = None
encoder_config_path = None
vc_path = None
vc_config_path = None
model_dir = None
# CASE1 #list : list pre-trained TTS models
if args.list_models:
manager.add_cs_api_models(api.list_models())
manager.list_models()
sys.exit()
# CASE2 #info : model info for pre-trained TTS models
if args.model_info_by_idx:
model_query = args.model_info_by_idx
manager.model_info_by_idx(model_query)
sys.exit()
if args.model_info_by_name:
model_query_full_name = args.model_info_by_name
manager.model_info_by_full_name(model_query_full_name)
sys.exit()
# CASE3: TTS with coqui studio models
if "coqui_studio" in args.model_name:
print(" > Using 🐸Coqui Studio model: ", args.model_name)
api = TTS(model_name=args.model_name, cs_api_model=args.cs_model)
api.tts_to_file(
text=args.text,
emotion=args.emotion,
file_path=args.out_path,
language=args.language,
speed=args.speed,
pipe_out=pipe_out,
)
print(" > Saving output to ", args.out_path)
return
# CASE4: load pre-trained model paths
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)
# CASE5: 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()