add readme

README.md

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+---
+language:
+  - as
+license: cc-by-sa-4.0
+library_name: f5-tts
+tags:
+  - text-to-speech
+  - tts
+  - f5-tts
+  - open-bible
+  - assamese
+pipeline_tag: text-to-speech
+base_model: SWivid/F5-TTS
+datasets:
+  - davidguzmanr/open-bible-resources
+inference: false
+---
+
+# F5-TTS Open Bible — Assamese
+
+A zero-shot text-to-speech model for **Assamese**, trained from scratch on
+the [Open Bible](https://huggingface.co/datasets/davidguzmanr/open-bible-resources)
+corpus using the [F5-TTS](https://github.com/SWivid/F5-TTS) architecture
+(diffusion transformer with vocos vocoder, 24 kHz output).
+
+The model takes a short reference audio clip (5–10 seconds) and a target text,
+and synthesises the target text in the voice of the reference speaker. No
+fine-tuning per voice is required.
+
+## Files
+
+| File | Purpose |
+|------|---------|
+| `model_last.pt` | Trained model weights. |
+| `vocab.txt` | Character vocabulary built from the training transcripts. |
+| `F5-TTS_OpenBible_Assamese.yaml` | Hydra training/inference config (architecture, mel spec settings, tokenizer). |
+
+## Intended use
+
+- Zero-shot TTS for Assamese, controlled by a user-supplied reference clip.
+- Research on multilingual TTS, low-resource TTS evaluation, and listening
+  studies on Open Bible–style read-speech.
+
+## How to use
+
+Install F5-TTS:
+
+```bash
+pip install git+https://github.com/SWivid/F5-TTS.git
+```
+
+Download the checkpoint and run inference:
+
+```python
+from huggingface_hub import hf_hub_download
+from f5_tts.api import F5TTS
+
+repo_id = "multilingual-tts/F5-TTS-OpenBible-Assamese"
+ckpt   = hf_hub_download(repo_id, "model_last.pt")
+vocab  = hf_hub_download(repo_id, "vocab.txt")
+config = hf_hub_download(repo_id, "F5-TTS_OpenBible_Assamese.yaml")
+
+model = F5TTS(ckpt_file=ckpt, vocab_file=vocab, model_cfg=config)
+
+# Supply your own clean reference clip — 5–10 s, single speaker and its transcription.
+ref_audio = "/path/to/your-assamese-clip.wav"
+ref_text  = "Exact transcription of the clip"
+gen_text  = "..."   # text to synthesise in Assamese
+
+wav, sr, _ = model.infer(ref_audio=ref_audio, ref_text=ref_text, gen_text=gen_text)
+```
+
+## Training data
+
+- **Source:** `davidguzmanr/open-bible-resources`, config `Assamese`
+- **Size:** approximately 30,500 utterances
+- **Speakers:** multispeaker; speaker identity is supplied at inference time
+  via the reference clip, not by a fixed speaker id
+- **Sample rate:** 24 kHz
+- **Maximum utterance duration during training:** 15 s
+
+## Training procedure
+
+- Base architecture: F5-TTS v1 Base (DiT, 1024 dim, 22 layers, 16 heads,
+  text dim 512, 4 convolutional layers).
+- Tokenizer: custom character-level, built from the training transcripts.
+- Vocoder: vocos.
+- Mel spectrogram: 100 channels, hop 256, win 1024, n_fft 1024.
+- Optimizer: AdamW, learning rate 7.5e-5, 20 000 warmup updates.
+- Training budget: 500,000 optimizer updates on 4 GPUs with mixed precision
+  (bf16), global batch ≈ 112,000 frames.
+
+Audio preprocessing, vocab generation, and config sizing are reproducible via
+the upstream
+[open-bible-models](https://github.com/davidguzmanr/open-bible-models) repo.
+
+## Evaluation
+
+Evaluated alongside other Open-Bible TTS systems on character/word error rate
+(via Meta's Omnilingual ASR) and UTMOSv2 naturalness scores. See the
+[open-bible-models](https://github.com/davidguzmanr/open-bible-models) repository
+for the evaluation pipeline and the
+[open-bible-surveys](https://github.com/davidguzmanr/open-bible-surveys) repository
+for the human-listening survey methodology.