def tts(model_name='tts_models/en/ljspeech/tacotron2-DCA',
vocoder_name=None,
use_cuda=False):
"""TTS entry point for PyTorch Hub that provides a Synthesizer object to synthesize speech from a give text.
Example:
>>> synthesizer = torch.hub.load('coqui-ai/TTS', 'tts', source='github')
>>> wavs = synthesizer.tts("This is a test! This is also a test!!")
wavs - is a list of values of the synthesized speech.
Args:
model_name (str, optional): One of the model names from .model.json. Defaults to 'tts_models/en/ljspeech/tacotron2-DCA'.
vocoder_name (str, optional): One of the model names from .model.json. Defaults to 'vocoder_models/en/ljspeech/multiband-melgan'.
pretrained (bool, optional): [description]. Defaults to True.
Returns:
TTS.utils.synthesizer.Synthesizer: Synthesizer object wrapping both vocoder and tts models.
"""
manager = ModelManager()
model_path, config_path, model_item = manager.download_model(model_name)
vocoder_name = model_item[
'default_vocoder'] if vocoder_name is None else vocoder_name
vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
# create synthesizer
synt = Synthesizer(tts_checkpoint=model_path,
tts_config_path=config_path,
vocoder_checkpoint=vocoder_path,
vocoder_config=vocoder_config_path,
use_cuda=use_cuda)
return synt
def __init__(self):
manager = ModelManager()
model_path, config_path, model_item = manager.download_model(MODEL)
vocoder_path, vocoder_config_path, _ = manager.download_model(
model_item['default_vocoder'])
# last arg is use kuda,
self.synth = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path, False)
def __init__(self, lang="en-us", config=None):
config = config or get_neon_tts_config().get("mozilla_local", {})
super(MozillaLocalTTS, self).__init__(lang,
config,
MozillaTTSValidator(self),
audio_ext="mp3",
ssml_tags=["speak"])
self.manager = ModelManager()
self.models = self.manager.list_models()
self.preferred_model = config.get("preferred_model", "tacotron2-DDC")
self._get_synthesizer(
lang) # Make sure we have a model available in init
def test_if_all_models_available():
"""Check if all the models are downloadable."""
print(
" > Checking the availability of all the models under the ModelManager."
)
manager = ModelManager(output_prefix=get_tests_output_path())
model_names = manager.list_models()
for model_name in model_names:
manager.download_model(model_name)
print(f" | > OK: {model_name}")
folders = glob.glob(os.path.join(manager.output_prefix, "*"))
assert len(folders) == len(model_names)
shutil.rmtree(manager.output_prefix)
def __init__(self, auto_start: bool = True):
super().__init__(auto_start)
path = Path(
__file__
).parent / "../../.venv/lib/python3.8/site-packages/TTS/.models.json"
manager = ModelManager(path)
model_path, config_path = manager.download_model(self.model_name)
vocoder_path, vocoder_config_path = manager.download_model(
self.vocoder_name)
self.synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path, self.use_cuda)
def __init__(self):
path = Path(synthesize.__file__).parent / "../.models.json"
logger.info("path")
logger.info("Creating ModelManager")
self.manager = ModelManager(path)
logger.info("Downloading model")
model_path, config_path, _ = self.manager.download_model(
self.MODEL_NAME)
logger.info("Downloading vcoder")
vocoder_path, vocoder_config_path, _ = self.manager.download_model(
self.VOCODER_NAME)
logger.info("Finished downloading TTS model & vcoder")
self.synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path, False)
self.tts_lock = threading.Lock()
def generate():
if inputbox.get("1.0", "end-1c") == "":
messagebox.showerror(
message=
"TTS will give a division by zero error if the text field is blank."
)
else:
if not os.path.exists('mozilla-tts-output'):
try:
os.makedirs('mozilla-tts-output')
except OSError as e:
if e.errno != errno.EEXIST:
raise
generatebutton.config(state="disabled")
exportbutton.config(state="disabled")
model_path = None
config_path = None
vocoder_path = None
vocoder_config_path = None
path = Path(__file__).parent / "TTS/.models.json"
manager = ModelManager(path)
model_name = 'tts_models/' + ttsmodelbox.get()
print(f'model_name is {model_name}')
# for dev
#model_path, config_path, model_item = manager.download_model(model_name)
# for master
model_path, config_path = manager.download_model(model_name)
vocoder_name = 'vocoder_models/' + vocodermodelbox.get()
print(f'vocoder_name is {vocoder_name}')
# for dev
#vocoder_path, vocoder_config_path, model_item = manager.download_model(vocoder_name)
# for master
vocoder_path, vocoder_config_path = manager.download_model(
vocoder_name)
synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path,
cudacheckbutton.instate(['selected']))
wav = synthesizer.tts(inputbox.get("1.0", "end-1c"))
synthesizer.save_wav(wav, "mozilla-tts-output/generated.wav")
playsound("mozilla-tts-output/generated.wav")
generatebutton.config(state="enabled")
exportbutton.config(state="enabled")
print("All done!")
class Tts:
MODEL_NAME = "tts_models/en/ljspeech/tacotron2-DCA"
VOCODER_NAME = "vocoder_models/en/ljspeech/multiband-melgan"
def __init__(self):
path = Path(synthesize.__file__).parent / "../.models.json"
logger.info("path")
logger.info("Creating ModelManager")
self.manager = ModelManager(path)
logger.info("Downloading model")
model_path, config_path, _ = self.manager.download_model(
self.MODEL_NAME)
logger.info("Downloading vcoder")
vocoder_path, vocoder_config_path, _ = self.manager.download_model(
self.VOCODER_NAME)
logger.info("Finished downloading TTS model & vcoder")
self.synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path, False)
self.tts_lock = threading.Lock()
def synthesize_speech(self, tts: str):
"""
This is largely copy pasted from TTS library (TTS.utils.synthesizer.Synthesizer.save_wav) but slightly modified
to allow NamedTemporaryFile as output instead of writing it to a file
:param tts: Text to speech
:return: Speech in NamedTemporaryFile (wav)
"""
if not self.tts_lock.acquire(blocking=True, timeout=0.1):
raise TTSAlreadyProcessingException
try:
wav = self.synthesizer.tts(tts)
wav = np.array(wav)
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
temp_file = NamedTemporaryFile(suffix=".wav")
scipy.io.wavfile.write(temp_file,
self.synthesizer.output_sample_rate,
wav_norm.astype(np.int16))
return temp_file
finally:
self.tts_lock.release()
def exportaudio():
if inputbox.get("1.0", "end-1c") == "":
messagebox.showerror(
message=
"TTS will give a division by zero error if the text field is blank."
)
else:
f = filedialog.asksaveasfile(mode='a',
defaultextension=".wav",
filetypes=[("Wave files", ".wav")])
if f is None: # asksaveasfile return `None` if dialog closed with "cancel".
return
generatebutton.config(state="disabled")
exportbutton.config(state="disabled")
model_path = None
config_path = None
vocoder_path = None
vocoder_config_path = None
path = Path(__file__).parent / "TTS/.models.json"
manager = ModelManager(path)
model_name = 'tts_models/' + ttsmodelbox.get()
print(f'model_name is {model_name}')
# for dev
#model_path, config_path, model_item = manager.download_model(model_name)
# for master
model_path, config_path = manager.download_model(model_name)
vocoder_name = 'vocoder_models/' + vocodermodelbox.get()
print(f'vocoder_name is {vocoder_name}')
# for dev
#vocoder_path, vocoder_config_path, model_item = manager.download_model(vocoder_name)
# for master
vocoder_path, vocoder_config_path = manager.download_model(vocoder_name)
synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path,
cudacheckbutton.instate(['selected']))
wav = synthesizer.tts(inputbox.get("1.0", "end-1c"))
synthesizer.save_wav(wav, str(f.name))
generatebutton.config(state="enabled")
exportbutton.config(state="enabled")
print("All done!")
def download_tts_model_and_vocodec():
path = Path(synthesize.__file__).parent / "../.models.json"
manager = ModelManager(path)
logger.info("Downloading model")
manager.download_model(Tts.MODEL_NAME)
logger.info("Downloading vcoder")
manager.download_model(Tts.VOCODER_NAME)
logger.info("Finished downloading TTS model & vocoder")
def make_synthesizer(model_name, use_cuda):
# load model manager
path = Path(TTS.__file__).parent / ".models.json"
manager = ModelManager(path)
model_path, config_path, model_item = manager.download_model(model_name)
vocoder_name = model_item["default_vocoder"]
vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
speakers_file_path = None
encoder_path = None
encoder_config_path = None
return Synthesizer(
model_path,
config_path,
speakers_file_path,
vocoder_path,
vocoder_config_path,
encoder_path,
encoder_config_path,
use_cuda,
)
def test_run_all_models():
"""Check if all the models are downloadable and tts models run correctly."""
print(" > Run synthesizer with all the models.")
download_dir = get_user_data_dir("tts")
output_path = os.path.join(get_tests_output_path(), "output.wav")
manager = ModelManager(output_prefix=get_tests_output_path())
model_names = manager.list_models()
for model_name in model_names:
print(f"\n > Run - {model_name}")
model_path, _, _ = manager.download_model(model_name)
if "tts_models" in model_name:
local_download_dir = os.path.dirname(model_path)
# download and run the model
speaker_files = glob.glob(local_download_dir + "/speaker*")
language_files = glob.glob(local_download_dir + "/language*")
language_id = ""
if len(speaker_files) > 0:
# multi-speaker model
if "speaker_ids" in speaker_files[0]:
speaker_manager = SpeakerManager(
speaker_id_file_path=speaker_files[0])
elif "speakers" in speaker_files[0]:
speaker_manager = SpeakerManager(
d_vectors_file_path=speaker_files[0])
# multi-lingual model - Assuming multi-lingual models are also multi-speaker
if len(language_files
) > 0 and "language_ids" in language_files[0]:
language_manager = LanguageManager(
language_ids_file_path=language_files[0])
language_id = language_manager.language_names[0]
speaker_id = list(speaker_manager.ids.keys())[0]
run_cli(
f"tts --model_name {model_name} "
f'--text "This is an example." --out_path "{output_path}" --speaker_idx "{speaker_id}" --language_idx "{language_id}" '
)
else:
# single-speaker model
run_cli(
f"tts --model_name {model_name} "
f'--text "This is an example." --out_path "{output_path}"')
# remove downloaded models
shutil.rmtree(download_dir)
else:
# only download the model
manager.download_model(model_name)
print(f" | > OK: {model_name}")
folders = glob.glob(os.path.join(manager.output_prefix, "*"))
assert len(folders) == len(model_names)
shutil.rmtree(manager.output_prefix)
def main():
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.
## Example Runs
### Single Speaker Models
- List provided models:
```
$ tts --list_models
```
- Query info for model info by idx:
```
$ tts --model_info_by_idx "<model_type>/<model_query_idx>"
```
- 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"
```
- Run a TTS model with its default vocoder model:
```
$ tts --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>"
```
- Run with specific TTS and vocoder models from the list:
```
$ tts --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>" --vocoder_name "<language>/<dataset>/<model_name>" --output_path
```
- 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/config.json --config_path path/to/model.pth --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 as <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/config.json --config_path path/to/model.pth --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
```
"""
# We remove Markdown code formatting programmatically here to allow us to copy-and-paste from main README to keep
# documentation in sync more easily.
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(
"--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 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,
)
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,
]
if not any(check_args):
parser.parse_args(["-h"])
# load model manager
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path)
model_path = None
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
# CASE1 #list : list pre-trained TTS models
if args.list_models:
manager.list_models()
sys.exit()
# CASE2 #info : model info of 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: 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)
args.vocoder_name = model_item["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
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:
model_path = args.model_path
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
# load models
synthesizer = Synthesizer(
model_path,
config_path,
speakers_file_path,
language_ids_file_path,
vocoder_path,
vocoder_config_path,
encoder_path,
encoder_config_path,
args.use_cuda,
)
# 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.ids)
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.ids)
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
wav = synthesizer.tts(
args.text,
args.speaker_idx,
args.language_idx,
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,
)
# save the results
print(" > Saving output to {}".format(args.out_path))
synthesizer.save_wav(wav, args.out_path)
from text import TextProcessor
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
from pydub import AudioSegment
import os
from pathlib import Path
import json
import justpy as jp
path = Path().cwd() / "TTS/TTS/.models.json"
manager = ModelManager(path)
print("> TTS Module Manager Loaded")
with open(path,"rt") as f:
modelsdict = json.load(f)
tts_models = [f"tts_models/{lang}/{dataset}" for lang in modelsdict["tts_models"] for dataset in modelsdict["tts_models"][lang]]
vocoder_models = [f"vocoder_models/{lang}/{dataset}" for lang in modelsdict["vocoder_models"] for dataset in modelsdict["vocoder_models"][lang]]
tp = TextProcessor()
print("> Text Processor Loaded")
def initsynthesizer(model_name,vocoder_name,use_cuda):
model_path, config_path = manager.download_model(model_name)
vocoder_path, vocoder_config_path = manager.download(vocoder_name)
return Synthesizer(model_path, config_path, vocoder_path, vocoder_config_path, use_cuda)
def tts(synth, text, out_name):
wav = synth.tts(text)
syth.save_wav(wav,out_name+'.wav')
AudioSegment.from_wav(out_name+'.wav').export(out_name+'.mp3',format="mp3")
os.remove(out_name+'.wav')
epochs=1000,
text_cleaner="english_cleaners",
use_phonemes=True,
use_espeak_phonemes=False,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=50,
print_eval=False,
mixed_precision=False,
sort_by_audio_len=True,
max_seq_len=500000,
output_path=output_path,
datasets=[dataset_config],
)
# compute alignments
if not config.model_args.use_aligner:
manager = ModelManager()
model_path, config_path, _ = manager.download_model(
"tts_models/en/ljspeech/tacotron2-DCA")
# TODO: make compute_attention python callable
os.system(
f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true"
)
# train the model
args, config, output_path, _, c_logger, tb_logger = init_training(
TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger)
trainer.fit()
def main():
# pylint: disable=bad-continuation
parser = argparse.ArgumentParser(description='''Synthesize speech on command line.\n\n'''
'''You can either use your trained model or choose a model from the provided list.\n\n'''\
'''If you don't specify any models, then it uses LJSpeech based English models\n\n'''\
'''
Example runs:
# list provided models
./TTS/bin/synthesize.py --list_models
# run tts with default models.
./TTS/bin synthesize.py --text "Text for TTS"
# run a tts model with its default vocoder model.
./TTS/bin synthesize.py --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>"
# run with specific tts and vocoder models from the list
./TTS/bin/synthesize.py --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>" --vocoder_name "<language>/<dataset>/<model_name>" --output_path
# run your own TTS model (Using Griffin-Lim Vocoder)
./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
# run your own TTS and Vocoder models
./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/config.json --config_path path/to/model.pth.tar --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
''',
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('--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/speedy-speech-wn",
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=Path(__file__).resolve().parent,
help=
'Path to save final wav file. Wav file will be named as the given text.',
)
parser.add_argument('--use_cuda',
type=bool,
help='Run model on CUDA.',
default=False)
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)
# args for multi-speaker synthesis
parser.add_argument('--speakers_json',
type=str,
help="JSON file for multi-speaker model.",
default=None)
parser.add_argument(
'--speaker_idx',
type=str,
help=
"if the tts model is trained with x-vectors, then speaker_idx is a file present in speakers.json else speaker_idx is the speaker id corresponding to a speaker in the speaker embedding layer.",
default=None)
parser.add_argument('--gst_style',
help="Wav path file for GST stylereference.",
default=None)
# aux args
parser.add_argument(
'--save_spectogram',
type=bool,
help=
"If true save raw spectogram for further (vocoder) processing in out_path.",
default=False)
args = parser.parse_args()
# print the description if either text or list_models is not set
if args.text is None and not args.list_models:
parser.parse_args(['-h'])
# load model manager
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path)
model_path = None
config_path = None
vocoder_path = None
vocoder_config_path = None
# CASE1: list pre-trained TTS models
if args.list_models:
manager.list_models()
sys.exit()
# CASE2: load pre-trained models
if args.model_name is not None:
model_path, config_path, model_item = manager.download_model(
args.model_name)
args.vocoder_name = model_item[
'default_vocoder'] if args.vocoder_name is None else args.vocoder_name
if args.vocoder_name is not None:
vocoder_path, vocoder_config_path, _ = manager.download_model(
args.vocoder_name)
# CASE3: load custome models
if args.model_path is not None:
model_path = args.model_path
config_path = args.config_path
if args.vocoder_path is not None:
vocoder_path = args.vocoder_path
vocoder_config_path = args.vocoder_config_path
# RUN THE SYNTHESIS
# load models
synthesizer = Synthesizer(model_path, config_path, vocoder_path,
vocoder_config_path, args.use_cuda)
print(" > Text: {}".format(args.text))
# # handle multi-speaker setting
# if not model_config.use_external_speaker_embedding_file and args.speaker_idx is not None:
# if args.speaker_idx.isdigit():
# args.speaker_idx = int(args.speaker_idx)
# else:
# args.speaker_idx = None
# else:
# args.speaker_idx = None
# if args.gst_style is None:
# if 'gst' in model_config.keys() and model_config.gst['gst_style_input'] is not None:
# gst_style = model_config.gst['gst_style_input']
# else:
# gst_style = None
# else:
# # check if gst_style string is a dict, if is dict convert else use string
# try:
# gst_style = json.loads(args.gst_style)
# if max(map(int, gst_style.keys())) >= model_config.gst['gst_style_tokens']:
# raise RuntimeError("The highest value of the gst_style dictionary key must be less than the number of GST Tokens, \n Highest dictionary key value: {} \n Number of GST tokens: {}".format(max(map(int, gst_style.keys())), model_config.gst['gst_style_tokens']))
# except ValueError:
# gst_style = args.gst_style
# kick it
wav = synthesizer.tts(args.text)
# save the results
file_name = args.text.replace(" ", "_")[0:20]
file_name = file_name.translate(
str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
out_path = os.path.join(args.out_path, file_name)
print(" > Saving output to {}".format(out_path))
synthesizer.save_wav(
wav,
out_path,
)
def main():
# pylint: disable=bad-option-value
parser = argparse.ArgumentParser(
description="""Synthesize speech on command line.\n\n"""
"""You can either use your trained model or choose a model from the provided list.\n\n"""
"""If you don't specify any models, then it uses LJSpeech based English model.\n\n"""
"""
# Example Runs:
## Single Speaker Models
- list provided models
```
$ ./TTS/bin/synthesize.py --list_models
```
- run tts with default models.
```
$ ./TTS/bin synthesize.py --text "Text for TTS"
```
- run a tts model with its default vocoder model.
```
$ ./TTS/bin synthesize.py --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>
```
- run with specific tts and vocoder models from the list
```
$ ./TTS/bin/synthesize.py --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>" --vocoder_name "<language>/<dataset>/<model_name>" --output_path
```
- run your own TTS model (Using Griffin-Lim Vocoder)
```
$ ./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
```
- run your own TTS and Vocoder models
```
$ ./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/config.json --config_path path/to/model.pth.tar --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
```
## MULTI-SPEAKER MODELS
- list the available speakers and choose as <speaker_id> among them.
```
$ ./TTS/bin/synthesize.py --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs
```
- run the multi-speaker TTS model with the target speaker ID.
```
$ ./TTS/bin/synthesize.py --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/bin/synthesize.py --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/config.json --config_path path/to/model.pth.tar --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
```
""",
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("--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(
"--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 multi-speaker synthesis
parser.add_argument("--speakers_file_path",
type=str,
help="JSON file for multi-speaker 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(
"--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 x_vectors is computed as their average.",
default=None,
)
parser.add_argument("--gst_style",
help="Wav path file for GST stylereference.",
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,
)
# aux args
parser.add_argument(
"--save_spectogram",
type=bool,
help=
"If true save raw spectogram for further (vocoder) processing in out_path.",
default=False,
)
args = parser.parse_args()
# print the description if either text or list_models is not set
if args.text is None and not args.list_models and not args.list_speaker_idxs:
parser.parse_args(["-h"])
# load model manager
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path)
model_path = None
config_path = None
speakers_file_path = None
vocoder_path = None
vocoder_config_path = None
encoder_path = None
encoder_config_path = None
# CASE1: list pre-trained TTS models
if args.list_models:
manager.list_models()
sys.exit()
# CASE2: 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)
args.vocoder_name = model_item[
"default_vocoder"] if args.vocoder_name is None else args.vocoder_name
if args.vocoder_name is not None and not args.vocoder_path:
vocoder_path, vocoder_config_path, _ = manager.download_model(
args.vocoder_name)
# CASE3: set custome model paths
if args.model_path is not None:
model_path = args.model_path
config_path = args.config_path
speakers_file_path = args.speakers_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
# load models
synthesizer = Synthesizer(
model_path,
config_path,
speakers_file_path,
vocoder_path,
vocoder_config_path,
encoder_path,
encoder_config_path,
args.use_cuda,
)
# 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.speaker_manager.speaker_ids)
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
print(" > Text: {}".format(args.text))
# kick it
wav = synthesizer.tts(args.text, args.speaker_idx, args.speaker_wav)
# save the results
print(" > Saving output to {}".format(args.out_path))
synthesizer.save_wav(wav, args.out_path)
help="path to JSON file containing speaker ids, if speaker ids are used in the model",
)
parser.add_argument("--vocoder_config", type=str, default=None, help="path to vocoder config file.")
parser.add_argument("--vocoder_checkpoint", type=str, default=None, help="path to vocoder checkpoint file.")
parser.add_argument("--port", type=int, default=5002, help="port to listen on.")
parser.add_argument("--use_cuda", type=convert_boolean, default=False, help="true to use CUDA.")
parser.add_argument("--debug", type=convert_boolean, default=False, help="true to enable Flask debug mode.")
parser.add_argument("--show_details", type=convert_boolean, default=False, help="Generate model detail page.")
return parser
# parse the args
args = create_argparser().parse_args()
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path)
if args.list_models:
manager.list_models()
sys.exit()
# update in-use models to the specified released models.
if args.model_name is not None:
tts_checkpoint_file, tts_config_file, tts_json_dict = manager.download_model(args.model_name)
args.vocoder_name = tts_json_dict["default_vocoder"] if args.vocoder_name is None else args.vocoder_name
if args.vocoder_name is not None:
vocoder_checkpoint_file, vocoder_config_file, vocoder_json_dict = manager.download_model(args.vocoder_name)
# If these were not specified in the CLI args, use default values with embedded model files
if not args.tts_checkpoint and os.path.isfile(tts_checkpoint_file):
parser.add_argument("--debug",
type=convert_boolean,
default=False,
help="true to enable Flask debug mode.")
parser.add_argument("--show_details",
type=convert_boolean,
default=False,
help="Generate model detail page.")
return parser
# parse the args
args = create_argparser().parse_args()
path = Path(__file__).parent / "../.models.json"
manager = ModelManager(path)
if args.list_models:
manager.list_models()
sys.exit()
# update in-use models to the specified released models.
model_path = None
config_path = None
speakers_file_path = None
vocoder_path = None
vocoder_config_path = None
# CASE1: list pre-trained TTS models
if args.list_models:
manager.list_models()
class MozillaLocalTTS(TTS):
def __init__(self, lang="en-us", config=None):
config = config or get_neon_tts_config().get("mozilla_local", {})
super(MozillaLocalTTS, self).__init__(lang,
config,
MozillaTTSValidator(self),
audio_ext="mp3",
ssml_tags=["speak"])
self.manager = ModelManager()
self.models = self.manager.list_models()
self.preferred_model = config.get("preferred_model", "tacotron2-DDC")
self._get_synthesizer(
lang) # Make sure we have a model available in init
def get_tts(self, sentence, wav_file, speaker=None):
stopwatch = Stopwatch()
speaker = speaker or dict()
# Read utterance data from passed configuration
request_lang = speaker.get("language", self.lang)
to_speak = format_speak_tags(sentence)
LOG.debug(to_speak)
if to_speak:
synthesizer = self._get_synthesizer(request_lang)
with stopwatch:
wav_data = synthesizer.tts(sentence)
LOG.debug(f"Synthesis time={stopwatch.time}")
with stopwatch:
synthesizer.save_wav(wav_data, wav_file)
LOG.debug(f"File access time={stopwatch.time}")
return wav_file, None
def _get_synthesizer(self, language) -> Synthesizer:
if '-' in language:
language = language.split('-')[0]
stopwatch = Stopwatch()
with stopwatch:
model_name = None
for model in self.models:
_, lang, dataset, name = model.split('/')
print(f"{lang}|{name}")
if language in lang:
model_name = model
if name == self.preferred_model:
break
model_path, config_path, model_item = self.manager.download_model(
model_name)
vocoder_name = model_item.get(
"default_vocoder",
"vocoder_models/universal/libri-tts/fullband-melgan")
vocoder_path, vocoder_config_path, _ = self.manager.download_model(
vocoder_name)
speakers_file_path = ''
encoder_path = ''
encoder_config_path = ''
use_cuda = False
synthesizer = Synthesizer(
model_path,
config_path,
speakers_file_path,
vocoder_path,
vocoder_config_path,
encoder_path,
encoder_config_path,
use_cuda,
)
LOG.debug(f"Get synthesizer time={stopwatch.time}")
return synthesizer
