Instructions to use facebook/mms-tts-eng with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/mms-tts-eng with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="facebook/mms-tts-eng")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") model = AutoModelForTextToWaveform.from_pretrained("facebook/mms-tts-eng") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -68,7 +68,7 @@ The resulting waveform can be saved as a `.wav` file:
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```python
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import scipy
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scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output.float().numpy())
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```
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Or displayed in a Jupyter Notebook / Google Colab:
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```python
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import scipy
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scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output.float().numpy().T)
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```
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Or displayed in a Jupyter Notebook / Google Colab:
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