app.py

import torch
import torchaudio
import gradio as gr
from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load processor & model
model_name = "cdactvm/w2v-bert-punjabi"  # Change if using a Punjabi ASR model
processor = Wav2Vec2BertProcessor.from_pretrained(model_name)
model = Wav2Vec2BertForCTC.from_pretrained(model_name, torch_dtype=torch.float16).to(device)

def transcribe(audio_path):
    # Load audio file
    waveform, sample_rate = torchaudio.load(audio_path)

    # Convert stereo to mono (if needed)
    if waveform.shape[0] > 1:
        waveform = torch.mean(waveform, dim=0, keepdim=True)

    # Resample to 16kHz
    if sample_rate != 16000:
        waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)

    # Process audio
    inputs = processor(waveform.squeeze(0), sampling_rate=16000, return_tensors="pt")
    inputs = {key: val.to(device, dtype=torch.bfloat16) for key, val in inputs.items()}

    # Get logits & transcribe
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)[0]

    return transcription

# Gradio Interface
app = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources="upload", type="filepath"),
    outputs="text",
    title="Punjabi Speech-to-Text",
    description="Upload an audio file and get the transcription in Punjabi."
)

if __name__ == "__main__":
    app.launch()


# import gradio as gr
# import torch
# from transformers import pipeline

# # Set device
# device = "cuda" if torch.cuda.is_available() else "cpu"

# # Load ASR pipeline
# asr_pipeline = pipeline(
#     "automatic-speech-recognition",
#     model="cdactvm/w2v-bert-punjabi",  # Replace with a Punjabi ASR model if available
#     torch_dtype=torch.bfloat16,
#     device=0 if torch.cuda.is_available() else -1  # GPU (0) or CPU (-1)
# )

# def transcribe(audio_path):
#     # Run inference
#     result = asr_pipeline(audio_path)
#     return result["text"]

# # Gradio Interface
# app = gr.Interface(
#     fn=transcribe,
#     inputs=gr.Audio(sources="upload", type="filepath"),
#     outputs="text",
#     title="Punjabi Speech-to-Text",
#     description="Upload an audio file and get the transcription in Punjabi."
# )

# if __name__ == "__main__":
#     app.launch()