mirror of https://github.com/coqui-ai/TTS.git
Eren Gölge
GitHub
commit
7f1a23787e
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@ -6,33 +6,53 @@ labels: bug
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assignees: ''
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---
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<!-- Welcome to the 🐸TTS!
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We are excited to see your interest, and appreciate your support! --->
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## 🐛 Description
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Welcome to the 🐸TTS project! We are excited to see your interest, and appreciate your support!
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<!-- A clear and concise description of what the bug is. -->
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This repository is governed by the Contributor Covenant Code of Conduct. For more details, see the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file.
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### To Reproduce
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If you've found a bug, please provide the following information:
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<!--
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Please share your code to reproduce the error. Issues fixed faster if you can provide a working example.
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**Describe the bug**
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A clear and concise description of what the bug is.
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The best place for sharing code is colab. https://colab.research.google.com/
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So we can directly run your code and reproduce the issue.
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In the worse case provide steps to reproduce the behaviour.
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**To Reproduce**
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Steps to reproduce the behavior:
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1. Run the following command '...'
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2. ...
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3. See error
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-->
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**Expected behavior**
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A clear and concise description of what you expected to happen.
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### Expected behavior
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**Environment (please complete the following information):**
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- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**:
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- **PyTorch or TensorFlow version (use command below)**:
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- **Python version**:
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- **CUDA/cuDNN version**:
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- **GPU model and memory**:
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- **Exact command to reproduce**:
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<!-- Write down what the expected behaviour -->
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**Additional context**
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Add any other context about the problem here.
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### Environment
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<!--
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You can either run `TTS/bin/collect_env_info.py`
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```bash
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wget https://raw.githubusercontent.com/coqui-ai/TTS/main/TTS/bin/collect_env_details.py
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python collect_env_details.py
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```
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or fill in the fields below manually.
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-->
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- 🐸TTS Version (e.g., 1.3.0):
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- PyTorch Version (e.g., 1.8)
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- Python version:
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- OS (e.g., Linux):
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- CUDA/cuDNN version:
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- GPU models and configuration:
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- How you installed PyTorch (`conda`, `pip`, source):
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- Any other relevant information:
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### Additional context
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<!-- Add any other context about the problem here. -->
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@ -2,25 +2,24 @@
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name: 🚀 Feature request
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about: Suggest a feature or an idea for this project
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title: '[Feature request] '
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labels: feature request
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labels: feature request
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assignees: ''
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---
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<!-- Welcome to the 🐸TTS project!
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We are excited to see your interest, and appreciate your support! --->
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||||
**🚀 Feature Description**
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||||
Welcome to the 🐸TTS project! We are excited to see your interest, and appreciate your support!
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||||
<!--A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] -->
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This repository is governed by the Contributor Covenant Code of Conduct. For more details, see the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file.
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**Solution**
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If you have a feature request, then please provide the following information:
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||||
<!-- A clear and concise description of what you want to happen. -->
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**Is your feature request related to a problem? Please describe.**
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A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
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**Alternative Solutions**
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**Describe the solution you'd like**
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A clear and concise description of what you want to happen.
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**Describe alternatives you've considered**
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A clear and concise description of any alternative solutions or features you've considered.
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||||
<!-- A clear and concise description of any alternative solutions or features you've considered. -->
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**Additional context**
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Add any other context or screenshots about the feature request here.
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<!-- Add any other context or screenshots about the feature request here. -->
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@ -98,7 +98,7 @@
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"fast_pitch":{
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"description": "FastPitch model trained on VCTK dataseset.",
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"github_rls_url": "https://coqui.gateway.scarf.sh/v0.4.0/tts_models--en--vctk--fast_pitch.zip",
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"default_vocoder": "vocoder_models/en/vctk/hifigan_v2",
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"default_vocoder": null,
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"commit": "bdab788d",
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"author": "Eren @erogol",
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"license": "CC BY-NC-ND 4.0",
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@ -0,0 +1,48 @@
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"""Get detailed info about the working environment."""
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import os
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import platform
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import sys
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import numpy
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import torch
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sys.path += [os.path.abspath(".."), os.path.abspath(".")]
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import json
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import TTS
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def system_info():
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return {
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"OS": platform.system(),
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"architecture": platform.architecture(),
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"version": platform.version(),
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"processor": platform.processor(),
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"python": platform.python_version(),
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}
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def cuda_info():
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return {
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"GPU": [torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())],
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"available": torch.cuda.is_available(),
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"version": torch.version.cuda,
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}
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def package_info():
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return {
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"numpy": numpy.__version__,
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"PyTorch_version": torch.__version__,
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"PyTorch_debug": torch.version.debug,
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"TTS": TTS.__version__,
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}
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def main():
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details = {"System": system_info(), "CUDA": cuda_info(), "Packages": package_info()}
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print(json.dumps(details, indent=4, sort_keys=True))
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if __name__ == "__main__":
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main()
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@ -254,7 +254,7 @@ def main():
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print(" > Text: {}".format(args.text))
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# kick it
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wav = synthesizer.tts(args.text, args.speaker_idx, args.speaker_wav)
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wav = synthesizer.tts(args.text, args.speaker_idx, args.speaker_wav, args.gst_style)
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# save the results
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print(" > Saving output to {}".format(args.out_path))
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@ -103,7 +103,7 @@ synthesizer = Synthesizer(
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model_path, config_path, speakers_file_path, vocoder_path, vocoder_config_path, use_cuda=args.use_cuda
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)
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use_multi_speaker = hasattr(synthesizer.tts_model, "speaker_manager") and synthesizer.tts_model.num_speakers > 1
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use_multi_speaker = hasattr(synthesizer.tts_model, "num_speakers") and synthesizer.tts_model.num_speakers > 1
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speaker_manager = getattr(synthesizer.tts_model, "speaker_manager", None)
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# TODO: set this from SpeakerManager
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use_gst = synthesizer.tts_config.get("use_gst", False)
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@ -284,8 +284,8 @@ class Trainer:
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self.optimizer = self.get_optimizer(self.model, self.config)
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# CALLBACK
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self.callbacks = TrainerCallback(self)
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self.callbacks.on_init_start()
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self.callbacks = TrainerCallback()
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self.callbacks.on_init_start(self)
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# init AMP
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if self.use_amp_scaler:
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@ -324,7 +324,7 @@ class Trainer:
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num_params = count_parameters(self.model)
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print("\n > Model has {} parameters".format(num_params))
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self.callbacks.on_init_end()
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self.callbacks.on_init_end(self)
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@staticmethod
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def parse_argv(args: Union[Coqpit, List]):
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@ -677,7 +677,7 @@ class Trainer:
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Returns:
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Tuple[Dict, Dict]: Model outputs and losses.
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"""
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self.callbacks.on_train_step_start()
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self.callbacks.on_train_step_start(self)
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# format data
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batch = self.format_batch(batch)
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loader_time = time.time() - loader_start_time
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@ -792,7 +792,7 @@ class Trainer:
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self.dashboard_logger.flush()
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self.total_steps_done += 1
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self.callbacks.on_train_step_end()
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self.callbacks.on_train_step_end(self)
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return outputs, loss_dict
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def train_epoch(self) -> None:
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@ -983,7 +983,7 @@ class Trainer:
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if self.num_gpus > 1:
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# let all processes sync up before starting with a new epoch of training
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dist.barrier()
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self.callbacks.on_epoch_start()
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self.callbacks.on_epoch_start(self)
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self.keep_avg_train = KeepAverage()
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self.keep_avg_eval = KeepAverage() if self.config.run_eval else None
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self.epochs_done = epoch
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@ -999,7 +999,7 @@ class Trainer:
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)
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if self.args.rank in [None, 0]:
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self.save_best_model()
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self.callbacks.on_epoch_end()
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self.callbacks.on_epoch_end(self)
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def fit(self) -> None:
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"""Where the ✨️magic✨️ happens..."""
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@ -1008,7 +1008,7 @@ class Trainer:
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if self.args.rank == 0:
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self.dashboard_logger.finish()
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except KeyboardInterrupt:
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self.callbacks.on_keyboard_interrupt()
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self.callbacks.on_keyboard_interrupt(self)
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# if the output folder is empty remove the run.
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remove_experiment_folder(self.output_path)
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# clear the DDP processes
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@ -110,6 +110,7 @@ class FastSpeechConfig(BaseTTSConfig):
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model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False)
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# multi-speaker settings
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num_speakers: int = 0
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speakers_file: str = None
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use_speaker_embedding: bool = False
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use_d_vector_file: bool = False
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@ -142,7 +143,7 @@ class FastSpeechConfig(BaseTTSConfig):
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r: int = 1 # DO NOT CHANGE
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# dataset configs
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compute_f0: bool = True
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compute_f0: bool = False
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f0_cache_path: str = None
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# testing
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@ -91,6 +91,8 @@ def load_tts_samples(
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for idx, ins in enumerate(meta_data_eval_all):
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attn_file = meta_data[ins[1]].strip()
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meta_data_eval_all[idx].append(attn_file)
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# set none for the next iter
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formatter = None
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return meta_data_train_all, meta_data_eval_all
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@ -110,3 +112,18 @@ def _get_formatter_by_name(name):
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"""Returns the respective preprocessing function."""
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thismodule = sys.modules[__name__]
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return getattr(thismodule, name.lower())
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def find_unique_chars(data_samples, verbose=True):
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texts = "".join(item[0] for item in data_samples)
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chars = set(texts)
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lower_chars = filter(lambda c: c.islower(), chars)
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chars_force_lower = [c.lower() for c in chars]
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chars_force_lower = set(chars_force_lower)
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if verbose:
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print(f" > Number of unique characters: {len(chars)}")
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print(f" > Unique characters: {''.join(sorted(chars))}")
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print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
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print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
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return chars_force_lower
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@ -60,7 +60,13 @@ def mozilla_de(root_path, meta_file):
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def mailabs(root_path, meta_files=None):
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"""Normalizes M-AI-Labs meta data files to TTS format"""
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"""Normalizes M-AI-Labs meta data files to TTS format
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Args:
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root_path (str): root folder of the MAILAB language folder.
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meta_files (str): list of meta files to be used in the training. If None, finds all the csv files
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recursively. Defaults to None
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"""
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speaker_regex = re.compile("by_book/(male|female)/(?P<speaker_name>[^/]+)/")
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if meta_files is None:
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csv_files = glob(root_path + "/**/metadata.csv", recursive=True)
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@ -266,7 +266,7 @@ class StochasticDurationPredictor(nn.Module):
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flows = list(reversed(self.flows))
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flows = flows[:-2] + [flows[-1]] # remove a useless vflow
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z = torch.rand(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
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z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
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for flow in flows:
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z = torch.flip(z, [1])
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z = flow(z, x_mask, g=x, reverse=reverse)
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@ -219,7 +219,7 @@ class BaseTTS(BaseModel):
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use_phonemes=config.use_phonemes,
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phoneme_language=config.phoneme_language,
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enable_eos_bos=config.enable_eos_bos_chars,
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use_noise_augment=not is_eval,
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use_noise_augment=False if is_eval else config.use_noise_augment,
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verbose=verbose,
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speaker_id_mapping=speaker_id_mapping,
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d_vector_mapping=d_vector_mapping if config.use_d_vector_file else None,
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@ -250,11 +250,11 @@ def synthesis(
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# GST processing
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style_mel = None
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custom_symbols = None
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if CONFIG.has("gst") and CONFIG.gst and style_wav is not None:
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if isinstance(style_wav, dict):
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style_mel = style_wav
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else:
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style_mel = compute_style_mel(style_wav, ap, cuda=use_cuda)
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if style_wav:
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style_mel = compute_style_mel(style_wav, ap, cuda=use_cuda)
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elif CONFIG.has("gst") and CONFIG.gst and not style_wav:
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if CONFIG.gst.gst_style_input_weights:
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style_mel = CONFIG.gst.gst_style_input_weights
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if hasattr(model, "make_symbols"):
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custom_symbols = model.make_symbols(CONFIG)
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# preprocess the given text
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|
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@ -5,6 +5,7 @@ import re
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from typing import Dict, List
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import gruut
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from gruut_ipa import IPA
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from TTS.tts.utils.text import cleaners
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from TTS.tts.utils.text.chinese_mandarin.phonemizer import chinese_text_to_phonemes
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@ -32,7 +33,7 @@ PHONEME_PUNCTUATION_PATTERN = r"[" + _punctuations.replace(" ", "") + "]+"
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GRUUT_TRANS_TABLE = str.maketrans("g", "ɡ")
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def text2phone(text, language, use_espeak_phonemes=False):
|
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def text2phone(text, language, use_espeak_phonemes=False, keep_stress=False):
|
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"""Convert graphemes to phonemes.
|
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Parameters:
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text (str): text to phonemize
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@ -51,36 +52,44 @@ def text2phone(text, language, use_espeak_phonemes=False):
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ph = japanese_text_to_phonemes(text)
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return ph
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if gruut.is_language_supported(language):
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# Use gruut for phonemization
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phonemizer_args = {
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"remove_stress": True,
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"ipa_minor_breaks": False, # don't replace commas/semi-colons with IPA |
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"ipa_major_breaks": False, # don't replace periods with IPA ‖
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}
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if not gruut.is_language_supported(language):
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raise ValueError(f" [!] Language {language} is not supported for phonemization.")
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if use_espeak_phonemes:
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# Use a lexicon/g2p model train on eSpeak IPA instead of gruut IPA.
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# This is intended for backwards compatibility with TTS<=v0.0.13
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# pre-trained models.
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phonemizer_args["model_prefix"] = "espeak"
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# Use gruut for phonemization
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ph_list = []
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for sentence in gruut.sentences(text, lang=language, espeak=use_espeak_phonemes):
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for word in sentence:
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if word.is_break:
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# Use actual character for break phoneme (e.g., comma)
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if ph_list:
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# Join with previous word
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ph_list[-1].append(word.text)
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else:
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# First word is punctuation
|
||||
ph_list.append([word.text])
|
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elif word.phonemes:
|
||||
# Add phonemes for word
|
||||
word_phonemes = []
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|
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ph_list = gruut.text_to_phonemes(
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||||
text,
|
||||
lang=language,
|
||||
return_format="word_phonemes",
|
||||
phonemizer_args=phonemizer_args,
|
||||
)
|
||||
for word_phoneme in word.phonemes:
|
||||
if not keep_stress:
|
||||
# Remove primary/secondary stress
|
||||
word_phoneme = IPA.without_stress(word_phoneme)
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||||
|
||||
# Join and re-split to break apart dipthongs, suprasegmentals, etc.
|
||||
ph_words = ["|".join(word_phonemes) for word_phonemes in ph_list]
|
||||
ph = "| ".join(ph_words)
|
||||
word_phoneme = word_phoneme.translate(GRUUT_TRANS_TABLE)
|
||||
|
||||
# Fix a few phonemes
|
||||
ph = ph.translate(GRUUT_TRANS_TABLE)
|
||||
return ph
|
||||
if word_phoneme:
|
||||
# Flatten phonemes
|
||||
word_phonemes.extend(word_phoneme)
|
||||
|
||||
raise ValueError(f" [!] Language {language} is not supported for phonemization.")
|
||||
if word_phonemes:
|
||||
ph_list.append(word_phonemes)
|
||||
|
||||
# Join and re-split to break apart dipthongs, suprasegmentals, etc.
|
||||
ph_words = ["|".join(word_phonemes) for word_phonemes in ph_list]
|
||||
ph = "| ".join(ph_words)
|
||||
|
||||
return ph
|
||||
|
||||
|
||||
def intersperse(sequence, token):
|
||||
|
|
|
|||
|
|
@ -674,7 +674,7 @@ class AudioProcessor(object):
|
|||
return f0
|
||||
|
||||
### Audio Processing ###
|
||||
def find_endpoint(self, wav: np.ndarray, threshold_db=-40, min_silence_sec=0.8) -> int:
|
||||
def find_endpoint(self, wav: np.ndarray, min_silence_sec=0.8) -> int:
|
||||
"""Find the last point without silence at the end of a audio signal.
|
||||
|
||||
Args:
|
||||
|
|
@ -687,7 +687,7 @@ class AudioProcessor(object):
|
|||
"""
|
||||
window_length = int(self.sample_rate * min_silence_sec)
|
||||
hop_length = int(window_length / 4)
|
||||
threshold = self._db_to_amp(threshold_db)
|
||||
threshold = self._db_to_amp(-self.trim_db)
|
||||
for x in range(hop_length, len(wav) - window_length, hop_length):
|
||||
if np.max(wav[x : x + window_length]) < threshold:
|
||||
return x + hop_length
|
||||
|
|
|
|||
|
|
@ -1,75 +1,105 @@
|
|||
class TrainerCallback:
|
||||
def __init__(self, trainer):
|
||||
super().__init__()
|
||||
self.trainer = trainer
|
||||
@staticmethod
|
||||
def on_init_start(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_init_start"):
|
||||
trainer.model.module.on_init_start(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_init_start"):
|
||||
trainer.model.on_init_start(trainer)
|
||||
|
||||
def on_init_start(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_init_start"):
|
||||
self.trainer.model.on_init_start(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_init_start"):
|
||||
trainer.criterion.on_init_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_init_start"):
|
||||
self.trainer.criterion.on_init_start(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_init_start"):
|
||||
trainer.optimizer.on_init_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_init_start"):
|
||||
self.trainer.optimizer.on_init_start(self.trainer)
|
||||
@staticmethod
|
||||
def on_init_end(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_init_end"):
|
||||
trainer.model.module.on_init_end(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_init_end"):
|
||||
trainer.model.on_init_end(trainer)
|
||||
|
||||
def on_init_end(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_init_end"):
|
||||
self.trainer.model.on_init_end(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_init_end"):
|
||||
trainer.criterion.on_init_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_init_end"):
|
||||
self.trainer.criterion.on_init_end(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_init_end"):
|
||||
trainer.optimizer.on_init_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_init_end"):
|
||||
self.trainer.optimizer.on_init_end(self.trainer)
|
||||
@staticmethod
|
||||
def on_epoch_start(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_epoch_start"):
|
||||
trainer.model.module.on_epoch_start(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_epoch_start"):
|
||||
trainer.model.on_epoch_start(trainer)
|
||||
|
||||
def on_epoch_start(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_epoch_start"):
|
||||
self.trainer.model.on_epoch_start(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_epoch_start"):
|
||||
trainer.criterion.on_epoch_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_epoch_start"):
|
||||
self.trainer.criterion.on_epoch_start(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_epoch_start"):
|
||||
trainer.optimizer.on_epoch_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_epoch_start"):
|
||||
self.trainer.optimizer.on_epoch_start(self.trainer)
|
||||
@staticmethod
|
||||
def on_epoch_end(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_epoch_end"):
|
||||
trainer.model.module.on_epoch_end(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_epoch_end"):
|
||||
trainer.model.on_epoch_end(trainer)
|
||||
|
||||
def on_epoch_end(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_epoch_end"):
|
||||
self.trainer.model.on_epoch_end(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_epoch_end"):
|
||||
trainer.criterion.on_epoch_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_epoch_end"):
|
||||
self.trainer.criterion.on_epoch_end(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_epoch_end"):
|
||||
trainer.optimizer.on_epoch_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_epoch_end"):
|
||||
self.trainer.optimizer.on_epoch_end(self.trainer)
|
||||
@staticmethod
|
||||
def on_train_step_start(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_train_step_start"):
|
||||
trainer.model.module.on_train_step_start(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_train_step_start"):
|
||||
trainer.model.on_train_step_start(trainer)
|
||||
|
||||
def on_train_step_start(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_train_step_start"):
|
||||
self.trainer.model.on_train_step_start(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_train_step_start"):
|
||||
trainer.criterion.on_train_step_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_train_step_start"):
|
||||
self.trainer.criterion.on_train_step_start(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_train_step_start"):
|
||||
trainer.optimizer.on_train_step_start(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_train_step_start"):
|
||||
self.trainer.optimizer.on_train_step_start(self.trainer)
|
||||
@staticmethod
|
||||
def on_train_step_end(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_train_step_end"):
|
||||
trainer.model.module.on_train_step_end(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_train_step_end"):
|
||||
trainer.model.on_train_step_end(trainer)
|
||||
|
||||
def on_train_step_end(self) -> None:
|
||||
if hasattr(trainer.criterion, "on_train_step_end"):
|
||||
trainer.criterion.on_train_step_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.model, "on_train_step_end"):
|
||||
self.trainer.model.on_train_step_end(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_train_step_end"):
|
||||
trainer.optimizer.on_train_step_end(trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_train_step_end"):
|
||||
self.trainer.criterion.on_train_step_end(self.trainer)
|
||||
@staticmethod
|
||||
def on_keyboard_interrupt(trainer) -> None:
|
||||
if hasattr(trainer.model, "module"):
|
||||
if hasattr(trainer.model.module, "on_keyboard_interrupt"):
|
||||
trainer.model.module.on_keyboard_interrupt(trainer)
|
||||
else:
|
||||
if hasattr(trainer.model, "on_keyboard_interrupt"):
|
||||
trainer.model.on_keyboard_interrupt(trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_train_step_end"):
|
||||
self.trainer.optimizer.on_train_step_end(self.trainer)
|
||||
if hasattr(trainer.criterion, "on_keyboard_interrupt"):
|
||||
trainer.criterion.on_keyboard_interrupt(trainer)
|
||||
|
||||
def on_keyboard_interrupt(self) -> None:
|
||||
if hasattr(self.trainer.model, "on_keyboard_interrupt"):
|
||||
self.trainer.model.on_keyboard_interrupt(self.trainer)
|
||||
|
||||
if hasattr(self.trainer.criterion, "on_keyboard_interrupt"):
|
||||
self.trainer.criterion.on_keyboard_interrupt(self.trainer)
|
||||
|
||||
if hasattr(self.trainer.optimizer, "on_keyboard_interrupt"):
|
||||
self.trainer.optimizer.on_keyboard_interrupt(self.trainer)
|
||||
if hasattr(trainer.optimizer, "on_keyboard_interrupt"):
|
||||
trainer.optimizer.on_keyboard_interrupt(trainer)
|
||||
|
|
|
|||
|
|
@ -74,6 +74,8 @@ class Synthesizer(object):
|
|||
if vocoder_checkpoint:
|
||||
self._load_vocoder(vocoder_checkpoint, vocoder_config, use_cuda)
|
||||
self.output_sample_rate = self.vocoder_config.audio["sample_rate"]
|
||||
else:
|
||||
print(" > Using Griffin-Lim as no vocoder model defined")
|
||||
|
||||
@staticmethod
|
||||
def _get_segmenter(lang: str):
|
||||
|
|
@ -265,7 +267,8 @@ class Synthesizer(object):
|
|||
waveform = waveform.squeeze()
|
||||
|
||||
# trim silence
|
||||
waveform = trim_silence(waveform, self.ap)
|
||||
if self.tts_config.audio["do_trim_silence"] is True:
|
||||
waveform = trim_silence(waveform, self.ap)
|
||||
|
||||
wavs += list(waveform)
|
||||
wavs += [0] * 10000
|
||||
|
|
|
|||
|
|
@ -310,6 +310,7 @@ class GAN(BaseVocoder):
|
|||
data_items: List,
|
||||
verbose: bool,
|
||||
num_gpus: int,
|
||||
rank: int = 0, # pylint: disable=unused-argument
|
||||
):
|
||||
"""Initiate and return the GAN dataloader.
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
|
||||
```
|
||||
|
||||
```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
|
||||
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
|
||||
```
|
||||
|
||||
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
|
||||
--restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/model_file.pth.tar
|
||||
--coqpit.run_name "glow-tts-finetune" \
|
||||
--coqpit.lr 0.00001
|
||||
```
|
||||
|
|
|
|||
|
|
@ -19,15 +19,15 @@ Let's assume you created the audio clips and their transcription. You can collec
|
|||
|
||||
You can either create separate transcription files for each clip or create a text file that maps each audio clip to its transcription. In this file, each line must be delimitered by a special character separating the audio file name from the transcription. And make sure that the delimiter is not used in the transcription text.
|
||||
|
||||
We recommend the following format delimited by `||`.
|
||||
We recommend the following format delimited by `||`. In the following example, `audio1`, `audio2` refer to files `audio1.wav`, `audio2.wav` etc.
|
||||
|
||||
```
|
||||
# metadata.txt
|
||||
|
||||
audio1.wav || This is my sentence.
|
||||
audio2.wav || This is maybe my sentence.
|
||||
audio3.wav || This is certainly my sentence.
|
||||
audio4.wav || Let this be your sentence.
|
||||
audio1||This is my sentence.
|
||||
audio2||This is maybe my sentence.
|
||||
audio3||This is certainly my sentence.
|
||||
audio4||Let this be your sentence.
|
||||
...
|
||||
```
|
||||
|
||||
|
|
@ -80,4 +80,4 @@ See `TTS.tts.datasets.TTSDataset`, a generic `Dataset` implementation for the `t
|
|||
See `TTS.vocoder.datasets.*`, for different `Dataset` implementations for the `vocoder` models.
|
||||
|
||||
See `TTS.utils.audio.AudioProcessor` that includes all the audio processing and feature extraction functions used in a
|
||||
`Dataset` implementation. Feel free to add things as you need.passed
|
||||
`Dataset` implementation. Feel free to add things as you need.passed
|
||||
|
|
|
|||
|
|
@ -35,7 +35,7 @@ config = Tacotron2Config( # This is the config that is saved for the future use
|
|||
test_delay_epochs=-1,
|
||||
r=2,
|
||||
# gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
|
||||
double_decoder_consistency=False,
|
||||
double_decoder_consistency=True,
|
||||
epochs=1000,
|
||||
text_cleaner="phoneme_cleaners",
|
||||
use_phonemes=True,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,87 @@
|
|||
import os
|
||||
|
||||
from TTS.config.shared_configs import BaseAudioConfig
|
||||
from TTS.trainer import Trainer, TrainingArgs
|
||||
from TTS.tts.configs.shared_configs import BaseDatasetConfig
|
||||
from TTS.tts.configs.tacotron2_config import Tacotron2Config
|
||||
from TTS.tts.datasets import load_tts_samples
|
||||
from TTS.tts.models.tacotron2 import Tacotron2
|
||||
from TTS.tts.utils.speakers import SpeakerManager
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
|
||||
output_path = os.path.dirname(os.path.abspath(__file__))
|
||||
dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
|
||||
|
||||
audio_config = BaseAudioConfig(
|
||||
sample_rate=22050,
|
||||
resample=False, # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
|
||||
do_trim_silence=True,
|
||||
trim_db=23.0,
|
||||
signal_norm=False,
|
||||
mel_fmin=0.0,
|
||||
mel_fmax=8000,
|
||||
spec_gain=1.0,
|
||||
log_func="np.log",
|
||||
preemphasis=0.0,
|
||||
)
|
||||
|
||||
config = Tacotron2Config( # This is the config that is saved for the future use
|
||||
audio=audio_config,
|
||||
batch_size=32,
|
||||
eval_batch_size=16,
|
||||
num_loader_workers=4,
|
||||
num_eval_loader_workers=4,
|
||||
run_eval=True,
|
||||
test_delay_epochs=-1,
|
||||
r=2,
|
||||
# gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
|
||||
double_decoder_consistency=False,
|
||||
epochs=1000,
|
||||
text_cleaner="phoneme_cleaners",
|
||||
use_phonemes=True,
|
||||
phoneme_language="en-us",
|
||||
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
|
||||
print_step=150,
|
||||
print_eval=False,
|
||||
mixed_precision=True,
|
||||
sort_by_audio_len=True,
|
||||
min_seq_len=14800,
|
||||
max_seq_len=22050 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
|
||||
output_path=output_path,
|
||||
datasets=[dataset_config],
|
||||
use_speaker_embedding=True, # set this to enable multi-sepeaker training
|
||||
decoder_ssim_alpha=0.0, # disable ssim losses that causes NaN for some runs.
|
||||
postnet_ssim_alpha=0.0,
|
||||
postnet_diff_spec_alpha=0.0,
|
||||
decoder_diff_spec_alpha=0.0,
|
||||
attention_norm="softmax",
|
||||
optimizer="Adam",
|
||||
lr_scheduler=None,
|
||||
lr=3e-5,
|
||||
)
|
||||
|
||||
# init audio processor
|
||||
ap = AudioProcessor(**config.audio.to_dict())
|
||||
|
||||
# load training samples
|
||||
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
|
||||
|
||||
# init speaker manager for multi-speaker training
|
||||
# it mainly handles speaker-id to speaker-name for the model and the data-loader
|
||||
speaker_manager = SpeakerManager()
|
||||
speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
|
||||
|
||||
# init model
|
||||
model = Tacotron2(config, speaker_manager)
|
||||
|
||||
# init the trainer and 🚀
|
||||
trainer = Trainer(
|
||||
TrainingArgs(),
|
||||
config,
|
||||
output_path,
|
||||
model=model,
|
||||
train_samples=train_samples,
|
||||
eval_samples=eval_samples,
|
||||
training_assets={"audio_processor": ap},
|
||||
)
|
||||
trainer.fit()
|
||||
|
|
@ -23,6 +23,6 @@ coqpit
|
|||
mecab-python3==1.0.3
|
||||
unidic-lite==1.0.8
|
||||
# gruut+supported langs
|
||||
gruut[cs,de,es,fr,it,nl,pt,ru,sv]~=1.2.0
|
||||
gruut[cs,de,es,fr,it,nl,pt,ru,sv]~=2.0.0
|
||||
fsspec>=2021.04.0
|
||||
pyworld
|
||||
pyworld
|
||||
|
|
|
|||
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@ -9,12 +9,12 @@ LANG = "en-us"
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||||
EXAMPLE_TEXT = "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase, the grey matter in the parts of the brain responsible for emotional regulation and learning!"
|
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|
||||
EXPECTED_PHONEMES = "ɹ|iː|s|ə|n|t| ɹ|ᵻ|s|ɜː|tʃ| æ|t| h|ɑːɹ|v|ɚ|d| h|æ|z| ʃ|oʊ|n| m|ɛ|d|ᵻ|t|eɪ|ɾ|ɪ|ŋ| f|ɔːɹ| æ|z| l|ɪ|ɾ|əl| æ|z| eɪ|t| w|iː|k|s| k|æ|n| æ|k|tʃ|uː|əl|i| ɪ|ŋ|k|ɹ|iː|s| ,| ð|ə| ɡ|ɹ|eɪ| m|æ|ɾ|ɚ| ɪ|n| ð|ə| p|ɑːɹ|t|s| ʌ|v| ð|ə| b|ɹ|eɪ|n| ɹ|ᵻ|s|p|ɑː|n|s|ᵻ|b|əl| f|ɔːɹ| ɪ|m|oʊ|ʃ|ə|n|əl| ɹ|ɛ|ɡ|j|ʊ|l|eɪ|ʃ|ə|n| æ|n|d| l|ɜː|n|ɪ|ŋ| !"
|
||||
EXPECTED_PHONEMES = "ɹ|i|ː|s|ə|n|t| ɹ|ᵻ|s|ɜ|ː|t|ʃ| æ|ɾ| h|ɑ|ː|ɹ|v|ɚ|d| h|ɐ|z| ʃ|o|ʊ|n| m|ɛ|d|ᵻ|t|e|ɪ|ɾ|ɪ|ŋ| f|ɔ|ː|ɹ| æ|z| l|ɪ|ɾ|ə|l| æ|z| e|ɪ|t| w|i|ː|k|s| k|æ|ŋ| æ|k|t|ʃ|u|ː|ə|l|i| ɪ|ŋ|k|ɹ|i|ː|s|,| ð|ə| ɡ|ɹ|e|ɪ| m|æ|ɾ|ɚ| ɪ|n| ð|ə| p|ɑ|ː|ɹ|t|s| ʌ|v| ð|ə| b|ɹ|e|ɪ|n| ɹ|ᵻ|s|p|ɑ|ː|n|s|ᵻ|b|ə|l| f|ɔ|ː|ɹ| ɪ|m|o|ʊ|ʃ|ə|n|ə|l| ɹ|ɛ|ɡ|j|ʊ|l|e|ɪ|ʃ|ə|n| æ|n|d| l|ɜ|ː|n|ɪ|ŋ|!"
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TextProcessingTextCase(unittest.TestCase):
|
||||
class TextProcessingTestCase(unittest.TestCase):
|
||||
"""Tests for text to phoneme conversion"""
|
||||
|
||||
def test_phoneme_to_sequence(self):
|
||||
|
|
@ -40,7 +40,7 @@ class TextProcessingTextCase(unittest.TestCase):
|
|||
sequence = phoneme_to_sequence(text, text_cleaner, LANG, add_blank=add_blank, use_espeak_phonemes=True)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence)
|
||||
gt = "biː ɐ vɔɪs , nɑːt ɐn ! ɛkoʊ ?"
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ?"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
self.assertEqual(text_hat, text_hat_with_params)
|
||||
|
|
@ -51,7 +51,7 @@ class TextProcessingTextCase(unittest.TestCase):
|
|||
sequence = phoneme_to_sequence(text, text_cleaner, LANG, add_blank=add_blank, use_espeak_phonemes=True)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence)
|
||||
gt = "biː ɐ vɔɪs , nɑːt ɐn ! ɛkoʊ"
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
self.assertEqual(text_hat, text_hat_with_params)
|
||||
|
|
@ -62,7 +62,7 @@ class TextProcessingTextCase(unittest.TestCase):
|
|||
sequence = phoneme_to_sequence(text, text_cleaner, LANG, add_blank=add_blank, use_espeak_phonemes=True)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence)
|
||||
gt = "biː ɐ vɔɪs , nɑːt ɐn ɛkoʊ !"
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn ɛkoʊ!"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
self.assertEqual(text_hat, text_hat_with_params)
|
||||
|
|
@ -73,7 +73,7 @@ class TextProcessingTextCase(unittest.TestCase):
|
|||
sequence = phoneme_to_sequence(text, text_cleaner, LANG, add_blank=add_blank, use_espeak_phonemes=True)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence)
|
||||
gt = "biː ɐ vɔɪs , nɑːt ɐn ! ɛkoʊ ."
|
||||
gt = "biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ."
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
self.assertEqual(text_hat, text_hat_with_params)
|
||||
|
|
@ -86,7 +86,7 @@ class TextProcessingTextCase(unittest.TestCase):
|
|||
)
|
||||
text_hat = sequence_to_phoneme(sequence)
|
||||
text_hat_with_params = sequence_to_phoneme(sequence)
|
||||
gt = "^biː ɐ vɔɪs , nɑːt ɐn ! ɛkoʊ .~"
|
||||
gt = "^biː ɐ vɔɪs, nɑːt ɐn! ɛkoʊ.~"
|
||||
print(text_hat)
|
||||
print(len(sequence))
|
||||
self.assertEqual(text_hat, text_hat_with_params)
|
||||
|
|
|
|||
Loading…
Reference in New Issue