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README.md

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+# Multi-Speaker VITS Model for Hausa
+
+This is a multi-speaker extension of the MMS-TTS Hausa model from Meta.
+
+## Model Details
+- **Base model**: facebook/mms-tts-hau
+- **Number of speakers**: 10
+- **Model class**: MultiSpeakerVITS
+- **Language**: Hausa (hau)
+- **Task**: Text-to-Speech (TTS)
+
+## Model Architecture
+
+This model extends the original MMS-TTS Hausa model with multi-speaker capabilities by:
+1. Adding speaker embeddings for 10 different speakers
+2. Conditioning the text encoder output with speaker information
+3. Maintaining compatibility with the original VITS architecture
+
+## Usage
+
+```python
+import torch
+from transformers import VitsModel, VitsTokenizer
+
+# Load the base model and tokenizer
+base_model = VitsModel.from_pretrained("facebook/mms-tts-hau")
+tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-hau")
+
+# Load the multi-speaker checkpoint
+checkpoint = torch.load("multispeaker_vits_template.pth")
+
+# Define the MultiSpeakerVITS class (copy from the original code)
+class MultiSpeakerVITS(torch.nn.Module):
+    # ... (copy the class definition from the original code)
+    pass
+
+# Create and load the multi-speaker model
+ms_model = MultiSpeakerVITS(base_model, n_speakers=10)
+ms_model.load_state_dict(checkpoint["model_state"])
+ms_model.eval()
+
+# Example usage
+text = "Sannu, ina kwana?"  # "Hello, how are you?" in Hausa
+inputs = tokenizer(text, return_tensors="pt")
+speaker_id = torch.tensor([0])  # Choose speaker 0-9
+
+with torch.no_grad():
+    output = ms_model(
+        input_ids=inputs["input_ids"],
+        attention_mask=inputs.get("attention_mask"),
+        speaker_ids=speaker_id
+    )
+```
+
+## Training
+
+This is a template model with initialized weights. To use it effectively, you'll need to:
+
+1. **Fine-tune on multi-speaker Hausa data**: Train the speaker embeddings and optionally fine-tune the base model
+2. **Prepare speaker-labeled dataset**: Each audio sample should be labeled with a speaker ID (0 to 9)
+3. **Training loop**: Implement a training loop that uses both text and speaker_ids as inputs
+
+## Files
+
+- `multispeaker_vits_template.pth`: PyTorch checkpoint containing model weights
+- `config.json`: Model configuration and metadata
+- `README.md`: This documentation
+
+## Citation
+
+```bibtex
+@article{pratap2023mms,
+  title={Scaling Speech Technology to 1,000+ Languages},
+  author={Pratap, Vineel and Tjandrawati, Andros and Conneau, Alexis and others},
+  journal={arXiv preprint arXiv:2305.13516},
+  year={2023}
+}
+```
+
+## License
+
+This model is based on the MMS-TTS model and follows the same licensing terms.