### Train YourTTS Model with Configured Parameters

Source: https://github.com/edresson/yourtts/blob/main/README.md

Execute this command to initiate the YourTTS model training process. Before running, ensure your 'config.json' file is properly configured with the path to the speaker embedding file ('d_vector_file.json'), the desired output path for checkpoints and logs, and the paths for the speaker encoder configuration and model. An optional '--restore_path' can be used for transfer learning.

```Python
python3 TTS/bin/train_tts.py --config_path config.json
```

--------------------------------

### BibTeX Citation for YourTTS Preprint (arXiv)

Source: https://github.com/edresson/yourtts/blob/main/README.md

This BibTeX entry provides the citation details for the YourTTS preprint published on arXiv. Use this entry when referencing the initial version of the YourTTS work in academic papers or projects.

```BibTeX
@ARTICLE{2021arXiv211202418C,
  author = {{Casanova}, Edresson and {Weber}, Julian and {Shulby}, Christopher and {Junior}, Arnaldo Candido and {G{\"o}lge}, Eren and {Antonelli Ponti}, Moacir},
  title = "{YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone}",
  journal = {arXiv e-prints},
  keywords = {Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing},
  year = 2021,
  month = dec,
  eid = {arXiv:2112.02418},
  pages = {arXiv:2112.02418},
  archivePrefix = {arXiv},
  eprint = {2112.02418},
  primaryClass = {cs.SD},
  adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv211202418C},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```

--------------------------------

### Perform Voice Conversion with YourTTS using Coqui TTS

Source: https://github.com/edresson/yourtts/blob/main/README.md

This command illustrates how to use the YourTTS model within Coqui TTS for zero-shot voice conversion. It requires the model name, a target speaker's audio sample, a reference audio file for content, and the language index to convert the content of the reference audio into the voice of the target speaker.

```Bash
tts --model_name tts_models/multilingual/multi-dataset/your_tts  --speaker_wav target_speaker_wav.wav --reference_wav  target_content_wav.wav --language_idx "en"
```

--------------------------------

### Perform Text-to-Speech with YourTTS using Coqui TTS

Source: https://github.com/edresson/yourtts/blob/main/README.md

This command demonstrates how to use the YourTTS model within Coqui TTS (v0.7.0 or higher) for zero-shot multi-speaker Text-to-Speech. It requires a text input, the model name, a target speaker's audio sample, and the language index to synthesize speech in the target speaker's voice.

```Bash
tts  --text "This is an example!" --model_name tts_models/multilingual/multi-dataset/your_tts  --speaker_wav target_speaker_wav.wav --language_idx "en"
```

--------------------------------

### BibTeX Citation for YourTTS Published Paper (ICML)

Source: https://github.com/edresson/yourtts/blob/main/README.md

This BibTeX entry provides the citation details for the YourTTS paper as published in the International Conference on Machine Learning (ICML). Use this entry when referencing the peer-reviewed and published version of the YourTTS work.

```BibTeX
@inproceedings{casanova2022yourtts,
  title={Yourtts: Towards zero-shot multi-speaker tts and zero-shot voice conversion for everyone},
  author={Casanova, Edresson and Weber, Julian and Shulby, Christopher D and Junior, Arnaldo Candido and G{\"o}lge, Eren and Ponti, Moacir A},
  booktitle={International Conference on Machine Learning},
  pages={2709--2720},
  year={2022},
  organization={PMLR}
}
```

--------------------------------

### Compute Speaker Embeddings for YourTTS Training

Source: https://github.com/edresson/yourtts/blob/main/README.md

This command extracts speaker embeddings from your dataset using the provided speaker encoder model and configuration. Ensure 'model_se.pth.tar' and 'config_se.json' are from the Coqui released model, and 'config.json' is adjusted to your dataset paths. The output 'd_vector_file.json' will contain the computed speaker embeddings.

```Python
python3 TTS/bin/compute_embeddings.py --model_path model_se.pth.tar --config_path config_se.json --config_dataset_path config.json --output_path d_vector_file.json
```

--------------------------------

### Recompute MOS for English

Source: https://github.com/edresson/yourtts/blob/main/metrics/MOS/README.md

Command to recalculate the Mean Opinion Score (MOS) with their respective confidence intervals for the English language. This command uses the 'compute_similarity_MOS.py' script and requires the path to the 'naturalness-MOS.csv' file.

```python
python3 compute_similarity_MOS.py --csv_path EN/naturalness-MOS.csv
```

--------------------------------

### Recompute Sim-MOS for English

Source: https://github.com/edresson/yourtts/blob/main/metrics/MOS/README.md

Command to recalculate the Similarity Mean Opinion Score (Sim-MOS) for the English language. This command uses the 'compute_similarity_MOS.py' script and requires the path to the 'Sim-MOS.csv' file.

```python
python3 compute_similarity_MOS.py --csv_path EN/Sim-MOS.csv
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.