Medix-AI/coqui-tts
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coqui-tts
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Code Issues Packages Projects Releases Wiki Activity

Update model file extension (#1422) 

* Update model file ext to ```.pth```

* Update docs

* Rename more

* Find model files
This commit is contained in:
Eren Gölge 2022-03-22 17:55:00 +01:00 committed by GitHub
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29 changed files with 74 additions and 103 deletions
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1
.gitignore vendored
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@ -115,6 +115,7 @@ venv.bak/
*.swo
# pytorch models
*.pth
*.pth.tar
result/

8
README.md
View File

@ -159,13 +159,13 @@ If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](ht
- Run your own TTS model (Using Griffin-Lim Vocoder):
```
$ tts --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
$ 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.tar --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
$ 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
@ -185,7 +185,7 @@ If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](ht
- 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.tar --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
$ 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>
```
## Directory Structure

2
TTS/bin/compute_attention_masks.py
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@ -25,7 +25,7 @@ These masks can be used for different purposes including training a TTS model wi
"""
Example run:
CUDA_VISIBLE_DEVICE="0" python TTS/bin/compute_attention_masks.py
--model_path /data/rw/home/Models/ljspeech-dcattn-December-14-2020_11+10AM-9d0e8c7/checkpoint_200000.pth.tar
--model_path /data/rw/home/Models/ljspeech-dcattn-December-14-2020_11+10AM-9d0e8c7/checkpoint_200000.pth
--config_path /data/rw/home/Models/ljspeech-dcattn-December-14-2020_11+10AM-9d0e8c7/config.json
--dataset_metafile metadata.csv
--data_path /root/LJSpeech-1.1/

2
TTS/bin/compute_embeddings.py
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@ -12,7 +12,7 @@ parser = argparse.ArgumentParser(
description="""Compute embedding vectors for each wav file in a dataset.\n\n"""
"""
Example runs:
python TTS/bin/compute_embeddings.py speaker_encoder_model.pth.tar speaker_encoder_config.json dataset_config.json embeddings_output_path/
python TTS/bin/compute_embeddings.py speaker_encoder_model.pth speaker_encoder_config.json dataset_config.json embeddings_output_path/
""",
formatter_class=RawTextHelpFormatter,
)

55
TTS/bin/distribute.py
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@ -1,55 +0,0 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import pathlib
import subprocess
import time
import torch
from trainer import TrainerArgs
def main():
"""
Call train.py as a new process and pass command arguments
"""
parser = TrainerArgs().init_argparse(arg_prefix="")
parser.add_argument("--script", type=str, help="Target training script to distibute.")
args, unargs = parser.parse_known_args()
num_gpus = torch.cuda.device_count()
group_id = time.strftime("%Y_%m_%d-%H%M%S")
# set arguments for train.py
folder_path = pathlib.Path(__file__).parent.absolute()
if os.path.exists(os.path.join(folder_path, args.script)):
command = [os.path.join(folder_path, args.script)]
else:
command = [args.script]
command.append("--continue_path={}".format(args.continue_path))
command.append("--restore_path={}".format(args.restore_path))
command.append("--config_path={}".format(args.config_path))
command.append("--group_id=group_{}".format(group_id))
command.append("--use_ddp=true")
command += unargs
command.append("")
# run a processes per GPU
processes = []
for i in range(num_gpus):
my_env = os.environ.copy()
my_env["PYTHON_EGG_CACHE"] = "/tmp/tmp{}".format(i)
command[-1] = "--rank={}".format(i)
# prevent stdout for processes with rank != 0
stdout = None
p = subprocess.Popen(["python3"] + command, stdout=stdout, env=my_env) # pylint: disable=consider-using-with
processes.append(p)
print(command)
for p in processes:
p.wait()
if __name__ == "__main__":
main()

2
TTS/bin/eval_encoder.py
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@ -56,7 +56,7 @@ if __name__ == "__main__":
description="""Compute the accuracy of the encoder.\n\n"""
"""
Example runs:
python TTS/bin/eval_encoder.py emotion_encoder_model.pth.tar emotion_encoder_config.json dataset_config.json
python TTS/bin/eval_encoder.py emotion_encoder_model.pth emotion_encoder_config.json dataset_config.json
""",
formatter_class=RawTextHelpFormatter,
)

8
TTS/bin/synthesize.py
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@ -60,13 +60,13 @@ If you don't specify any models, then it uses LJSpeech based English model.
- Run your own TTS model (Using Griffin-Lim Vocoder):
```
$ tts --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
$ 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.tar --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
$ 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
@ -86,7 +86,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
- 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.tar --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
$ 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

2
TTS/encoder/README.md
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@ -14,5 +14,5 @@ To run the code, you need to follow the same flow as in TTS.
- Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
- Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360```
- Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth.tar model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
- Generate embedding vectors ```python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth model/config/path/config.json dataset/path/ output_path``` . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
- Watch training on Tensorboard as in TTS

4
TTS/encoder/utils/generic_utils.py
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@ -147,7 +147,7 @@ def setup_speaker_encoder_model(config: "Coqpit"):
def save_checkpoint(model, optimizer, criterion, model_loss, out_path, current_step, epoch):
checkpoint_path = "checkpoint_{}.pth.tar".format(current_step)
checkpoint_path = "checkpoint_{}.pth".format(current_step)
checkpoint_path = os.path.join(out_path, checkpoint_path)
print(" | | > Checkpoint saving : {}".format(checkpoint_path))
@ -177,7 +177,7 @@ def save_best_model(model, optimizer, criterion, model_loss, best_loss, out_path
"date": datetime.date.today().strftime("%B %d, %Y"),
}
best_loss = model_loss
bestmodel_path = "best_model.pth.tar"
bestmodel_path = "best_model.pth"
bestmodel_path = os.path.join(out_path, bestmodel_path)
print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
save_fsspec(state, bestmodel_path)

4
TTS/encoder/utils/io.py
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@ -5,7 +5,7 @@ from TTS.utils.io import save_fsspec
def save_checkpoint(model, optimizer, model_loss, out_path, current_step):
checkpoint_path = "checkpoint_{}.pth.tar".format(current_step)
checkpoint_path = "checkpoint_{}.pth".format(current_step)
checkpoint_path = os.path.join(out_path, checkpoint_path)
print(" | | > Checkpoint saving : {}".format(checkpoint_path))
@ -31,7 +31,7 @@ def save_best_model(model, optimizer, model_loss, best_loss, out_path, current_s
"date": datetime.date.today().strftime("%B %d, %Y"),
}
best_loss = model_loss
bestmodel_path = "best_model.pth.tar"
bestmodel_path = "best_model.pth"
bestmodel_path = os.path.join(out_path, bestmodel_path)
print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
save_fsspec(state, bestmodel_path)

2
TTS/server/README.md
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@ -21,4 +21,4 @@ Run the server with the official models on a GPU.
```CUDA_VISIBLE_DEVICES="0" python TTS/server/server.py --model_name tts_models/en/ljspeech/tacotron2-DCA --vocoder_name vocoder_models/en/ljspeech/multiband-melgan --use_cuda True```
Run the server with a custom models.
```python TTS/server/server.py --tts_checkpoint /path/to/tts/model.pth.tar --tts_config /path/to/tts/config.json --vocoder_checkpoint /path/to/vocoder/model.pth.tar --vocoder_config /path/to/vocoder/config.json```
```python TTS/server/server.py --tts_checkpoint /path/to/tts/model.pth --tts_config /path/to/tts/config.json --vocoder_checkpoint /path/to/vocoder/model.pth --vocoder_config /path/to/vocoder/config.json```

2
TTS/server/conf.json
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@ -1,6 +1,6 @@
{
"tts_path":"/media/erogol/data_ssd/Models/libri_tts/5049/", // tts model root folder
"tts_file":"best_model.pth.tar", // tts checkpoint file
"tts_file":"best_model.pth", // tts checkpoint file
"tts_config":"config.json", // tts config.json file
"tts_speakers": null, // json file listing speaker ids. null if no speaker embedding.
"vocoder_config":null,

2
TTS/utils/audio.py
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@ -371,7 +371,7 @@ class AudioProcessor(object):
self.hop_length = hop_length
self.win_length = win_length
assert min_level_db != 0.0, " [!] min_level_db is 0"
assert self.win_length <= self.fft_size, " [!] win_length cannot be larger than fft_size"
assert self.win_length <= self.fft_size, f" [!] win_length cannot be larger than fft_size - {self.win_length} vs {self.fft_size}"
members = vars(self)
if verbose:
print(" > Setting up Audio Processor...")

2
TTS/utils/generic_utils.py
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@ -67,7 +67,7 @@ def get_experiment_folder_path(root_path, model_name):
def remove_experiment_folder(experiment_path):
"""Check folder if there is a checkpoint, otherwise remove the folder"""
fs = fsspec.get_mapper(experiment_path).fs
checkpoint_files = fs.glob(experiment_path + "/*.pth.tar")
checkpoint_files = fs.glob(experiment_path + "/*.pth")
if not checkpoint_files:
if fs.exists(experiment_path):
fs.rm(experiment_path, recursive=True)

8
TTS/utils/io.py
View File

@ -140,7 +140,7 @@ def save_checkpoint(
output_folder,
**kwargs,
):
file_name = "checkpoint_{}.pth.tar".format(current_step)
file_name = "checkpoint_{}.pth".format(current_step)
checkpoint_path = os.path.join(output_folder, file_name)
print("\n > CHECKPOINT : {}".format(checkpoint_path))
save_model(
@ -170,7 +170,7 @@ def save_best_model(
**kwargs,
):
if current_loss < best_loss:
best_model_name = f"best_model_{current_step}.pth.tar"
best_model_name = f"best_model_{current_step}.pth"
checkpoint_path = os.path.join(out_path, best_model_name)
print(" > BEST MODEL : {}".format(checkpoint_path))
save_model(
@ -187,12 +187,12 @@ def save_best_model(
fs = fsspec.get_mapper(out_path).fs
# only delete previous if current is saved successfully
if not keep_all_best or (current_step < keep_after):
model_names = fs.glob(os.path.join(out_path, "best_model*.pth.tar"))
model_names = fs.glob(os.path.join(out_path, "best_model*.pth"))
for model_name in model_names:
if os.path.basename(model_name) != best_model_name:
fs.rm(model_name)
# create a shortcut which always points to the currently best model
shortcut_name = "best_model.pth.tar"
shortcut_name = "best_model.pth"
shortcut_path = os.path.join(out_path, shortcut_name)
fs.copy(checkpoint_path, shortcut_path)
best_loss = current_loss

31
TTS/utils/manage.py
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@ -3,6 +3,7 @@ import json
import os
import zipfile
from pathlib import Path
from typing import Tuple
from shutil import copyfile, rmtree
import requests
@ -114,7 +115,7 @@ class ModelManager(object):
e.g. 'tts_model/en/ljspeech/tacotron'
Every model must have the following files:
- *.pth.tar : pytorch model checkpoint file.
- *.pth : pytorch model checkpoint file.
- config.json : model config file.
- scale_stats.npy (if exist): scale values for preprocessing.
@ -127,7 +128,7 @@ class ModelManager(object):
model_item = self.models_dict[model_type][lang][dataset][model]
# set the model specific output path
output_path = os.path.join(self.output_prefix, model_full_name)
output_model_path = os.path.join(output_path, "model_file.pth.tar")
output_model_path = os.path.join(output_path, "model_file.pth")
output_config_path = os.path.join(output_path, "config.json")
if os.path.exists(output_path):
@ -139,8 +140,32 @@ class ModelManager(object):
self._download_zip_file(model_item["github_rls_url"], output_path)
# update paths in the config.json
self._update_paths(output_path, output_config_path)
# find downloaded files
output_model_path, output_config_path = self._find_files(output_path)
return output_model_path, output_config_path, model_item
def _find_files(self, output_path:str) -> Tuple[str, str]:
"""Find the model and config files in the output path
Args:
output_path (str): path to the model files
Returns:
Tuple[str, str]: path to the model file and config file
"""
model_file = None
config_file = None
for file_name in os.listdir(output_path):
if file_name in ["model_file.pth", "model_file.pth.tar", "model.pth"]:
model_file = os.path.join(output_path, file_name)
elif file_name == "config.json":
config_file = os.path.join(output_path, file_name)
if model_file is None:
raise ValueError(" [!] Model file not found in the output path")
if config_file is None:
raise ValueError(" [!] Config file not found in the output path")
return model_file, config_file
def _update_paths(self, output_path: str, config_path: str) -> None:
"""Update paths for certain files in config.json after download.
@ -152,7 +177,7 @@ class ModelManager(object):
output_d_vector_file_path = os.path.join(output_path, "speakers.json")
output_speaker_ids_file_path = os.path.join(output_path, "speaker_ids.json")
speaker_encoder_config_path = os.path.join(output_path, "config_se.json")
speaker_encoder_model_path = os.path.join(output_path, "model_se.pth.tar")
speaker_encoder_model_path = os.path.join(output_path, "model_se.pth")
# update the scale_path.npy file path in the model config.json
self._update_path("audio.stats_path", output_stats_path, config_path)

2
TTS/vocoder/README.md
View File

@ -29,7 +29,7 @@ You can continue a previous training run by the following command.
You can fine-tune a pre-trained model by the following command.
```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth.tar```
```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth```
Restoring a model starts a new training in a different folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same directory where the previous training run left off.

6
docs/source/finetuning.md
View File

@ -93,13 +93,13 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
```bash
CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
```
```bash
CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py \
--config_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/config.json \
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
```
As stated above, you can also use command-line arguments to change the model configuration.
@ -107,7 +107,7 @@ them and fine-tune it for your own dataset. This will help you in two main ways:
```bash
CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth
--coqpit.run_name "glow-tts-finetune" \
--coqpit.lr 0.00001
```

6
docs/source/inference.md
View File

@ -44,7 +44,7 @@ Run your own TTS model (Using Griffin-Lim Vocoder)
```bash
tts --text "Text for TTS" \
--model_path path/to/model.pth.tar \
--model_path path/to/model.pth \
--config_path path/to/config.json \
--out_path folder/to/save/output.wav
```
@ -54,9 +54,9 @@ Run your own TTS and Vocoder models
```bash
tts --text "Text for TTS" \
--config_path path/to/config.json \
--model_path path/to/model.pth.tar \
--model_path path/to/model.pth \
--out_path folder/to/save/output.wav \
--vocoder_path path/to/vocoder.pth.tar \
--vocoder_path path/to/vocoder.pth \
--vocoder_config_path path/to/vocoder_config.json
```

4
docs/source/training_a_model.md
View File

@ -33,7 +33,7 @@
If you like to run a multi-gpu training using DDP back-end,
```bash
$ CUDA_VISIBLE_DEVICES="0, 1, 2" python TTS/bin/distribute.py --script <path_to_your_script>/train_glowtts.py
$ CUDA_VISIBLE_DEVICES="0, 1, 2" python -m trainer.distribute --script <path_to_your_script>/train_glowtts.py
```
The example above runs a multi-gpu training using GPUs `0, 1, 2`.
@ -122,7 +122,7 @@
```bash
$ tts --text "Text for TTS" \
--model_path path/to/checkpoint_x.pth.tar \
--model_path path/to/checkpoint_x.pth \
--config_path path/to/config.json \
--out_path folder/to/save/output.wav
```

4
docs/source/tutorial_for_nervous_beginners.md
View File

@ -50,13 +50,13 @@ A breakdown of a simple script that trains a GlowTTS model on the LJspeech datas
- Fine-tune a model.
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth.tar
CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth
```
- Run multi-gpu training.
```bash
CUDA_VISIBLE_DEVICES=0,1,2 python TTS/bin/distribute.py --script train.py
CUDA_VISIBLE_DEVICES=0,1,2 python -m trainer.distribute --script train.py
```
### CLI Way

2
notebooks/ExtractTTSpectrogram.ipynb
View File

@ -66,7 +66,7 @@
"DATASET = \"ljspeech\"\n",
"METADATA_FILE = \"metadata.csv\"\n",
"CONFIG_PATH = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/config.json\"\n",
"MODEL_FILE = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/model_file.pth.tar\"\n",
"MODEL_FILE = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/model_file.pth\"\n",
"BATCH_SIZE = 32\n",
"\n",
"QUANTIZED_WAV = False\n",

2
notebooks/PlotUmapLibriTTS.ipynb
View File

@ -66,7 +66,7 @@
"outputs": [],
"source": [
"MODEL_RUN_PATH = \"/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/libritts_360-half-October-31-2019_04+54PM-19d2f5f/\"\n",
"MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
"MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth\"\n",
"CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
"\n",
"# My single speaker locations\n",

2
notebooks/TestAttention.ipynb
View File

@ -73,7 +73,7 @@
"\n",
"# Set constants\n",
"ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-May-20-2020_12+29PM-1835628/'\n",
"MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
"MODEL_PATH = ROOT_PATH + '/best_model.pth'\n",
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
"OUT_FOLDER = './hard_sentences/'\n",
"CONFIG = load_config(CONFIG_PATH)\n",

2
notebooks/dataset_analysis/AnalyzeDataset.ipynb
View File

@ -416,7 +416,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.9.5"
}
},
"nbformat": 4,

6
tests/aux_tests/test_extract_tts_spectrograms.py
View File

@ -15,7 +15,7 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
def test_GlowTTS():
# set paths
config_path = os.path.join(get_tests_input_path(), "test_glow_tts.json")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth")
output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
# load config
c = load_config(config_path)
@ -33,7 +33,7 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
def test_Tacotron2():
# set paths
config_path = os.path.join(get_tests_input_path(), "test_tacotron2_config.json")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth")
output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
# load config
c = load_config(config_path)
@ -51,7 +51,7 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
def test_Tacotron():
# set paths
config_path = os.path.join(get_tests_input_path(), "test_tacotron_config.json")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth")
output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
# load config
c = load_config(config_path)

2
tests/aux_tests/test_speaker_manager.py
View File

@ -12,7 +12,7 @@ from TTS.tts.utils.speakers import SpeakerManager
from TTS.utils.audio import AudioProcessor
encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth.tar")
encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth")
sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
d_vectors_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")

2
tests/inference_tests/test_synthesizer.py
View File

@ -20,7 +20,7 @@ class SynthesizerTest(unittest.TestCase):
def test_in_out(self):
self._create_random_model()
tts_root_path = get_tests_output_path()
tts_checkpoint = os.path.join(tts_root_path, "checkpoint_10.pth.tar")
tts_checkpoint = os.path.join(tts_root_path, "checkpoint_10.pth")
tts_config = os.path.join(tts_root_path, "dummy_model_config.json")
synthesizer = Synthesizer(tts_checkpoint, tts_config, None, None)
synthesizer.tts("Better this test works!!")

2
tests/inputs/server_config.json
View File

@ -1,5 +1,5 @@
{
"tts_checkpoint":"checkpoint_10.pth.tar", // tts checkpoint file
"tts_checkpoint":"checkpoint_10.pth", // tts checkpoint file
"tts_config":"dummy_model_config.json", // tts config.json file
"tts_speakers": null, // json file listing speaker ids. null if no speaker embedding.
"wavernn_lib_path": null, // Rootpath to wavernn project folder to be imported. If this is null, model uses GL for speech synthesis.