| import os |
| import torch |
| import gradio as gr |
| from transformers import AutoModel |
| import numpy as np |
|
|
| MODEL_NAME = "ARTPARK-IISc/SraVaani" |
|
|
| print("Loading model, this may take a few minutes...") |
| model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True, token=os.getenv("HF_TOKEN")) |
| model.eval() |
| print("Model loaded successfully.") |
|
|
|
|
| def transcribe(audio_input): |
| if audio_input is None: |
| return "" |
|
|
| sr, audio = audio_input |
|
|
| if audio.ndim == 2: |
| audio = np.mean(audio, axis=1) |
|
|
| audio = audio.astype(np.float32) |
| audio = audio / (np.max(np.abs(audio)) + 1e-9) |
|
|
| import tempfile, soundfile as sf |
| with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f: |
| sf.write(f.name, audio, sr) |
| results = model.transcribe([f.name]) |
|
|
| return results[0] if results else "" |
|
|
|
|
| demo = gr.Interface( |
| fn=transcribe, |
| inputs=gr.Audio( |
| sources=["microphone", "upload"], |
| type="numpy", |
| label="Record or upload WAV audio" |
| ), |
| outputs=gr.Textbox(label="Transcription"), |
| title="SraVaani Multilingual ASR", |
| description="Upload a WAV file and get the multilingual ASR transcription." |
| ) |
|
|
| demo.launch(share=True) |
