app.py

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

model = AutoModelForCTC.from_pretrained("anzorq/output")
processor = Wav2Vec2BertProcessor.from_pretrained("anzorq/output")

def transcribe_speech(audio):
    # Load the audio file
    waveform, sr = torchaudio.load(audio)

    # Resample the audio if needed
    resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)
    waveform = resampler(waveform)

    # Convert to mono if needed
    if waveform.dim() > 1:
        waveform = torch.mean(waveform, dim=0)

    # Normalize the audio
    waveform = waveform / torch.max(torch.abs(waveform))

    # Extract input features
    input_features = processor(waveform.unsqueeze(0), sampling_rate=16000).input_features
    input_features = torch.from_numpy(input_features).to("cuda" if torch.cuda.is_available() else "cpu")

    # Generate logits using the model
    with torch.no_grad():
        logits = model(input_features).logits

    # Decode the predicted ids to text
    pred_ids = torch.argmax(logits, dim=-1)[0]
    pred_text = processor.decode(pred_ids)

    return pred_text

# Define the Gradio interface
interface = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs="text",
    live=True,
)

# Launch the app
interface.launch()