Instructions to use Renderlib-dev/sooktam2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Renderlib-dev/sooktam2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Renderlib-dev/sooktam2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Renderlib-dev/sooktam2", trust_remote_code=True, dtype="auto") - F5-TTS
How to use Renderlib-dev/sooktam2 with F5-TTS:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| import os | |
| from transformers import AutoModel | |
| # --- Paths / model id (adjust if needed) --- | |
| REPO_DIR = "." | |
| MODEL_ID = "bharatgenai/sooktam2" | |
| REF_AUDIO = "./ref.wav" | |
| REF_TEXT = "सर, मैं तब से यह कह रहा हूँ कि मैंने अपना टिकट कैंसल कर दिया है, लेकिन अब तक मेरे पैसे वापस नहीं आए हैं। आप इस मामले को देखेंगे भी या नहीं?" | |
| GEN_TEXT = "यह एक टेस्ट वाक्य है जिसे आवाज़ में बदलना है।" | |
| OUT_DIR = os.path.join(REPO_DIR, "outputs") | |
| OUT_WAV = os.path.join(OUT_DIR, "sooktam_cls.wav") | |
| # CLS tokenization is handled inside utils_infer via cls_tokenizer_v2. | |
| # --- Load TTS model via AutoModel (auto-download ckpt + vocab from HF) --- | |
| model = AutoModel.from_pretrained( | |
| MODEL_ID, | |
| trust_remote_code=True, | |
| ) | |
| os.makedirs(OUT_DIR, exist_ok=True) | |
| wav, sr, _ = model.infer( | |
| ref_file=REF_AUDIO, | |
| ref_text=REF_TEXT, | |
| gen_text=GEN_TEXT, | |
| tokenizer="cls", | |
| cls_language="hindi", | |
| file_wave=OUT_WAV, | |
| ) | |
| print("Saved:", OUT_WAV, "sample_rate:", sr, "samples:", len(wav)) | |