app.py

import gradio as gr
import nemo.collections.asr as nemo_asr
import numpy as np
import torch

# Load the pre-trained Kabyle ASR model
asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_kab_conformer_transducer_large")

# Function to transcribe the audio input
def transcribe(audio):
    # Print the raw audio input
    print(f"Raw audio input: {audio}")

    # Audio in Gradio is returned as a tuple (sample_rate, audio_data)
    sample_rate, audio_data = audio

    # Print to check the types
    print(f"Audio data type: {type(audio_data)}")
    print(f"Sample rate type: {type(sample_rate)}")

    # Ensure the audio data is in numpy array format
    if isinstance(audio_data, np.ndarray):
        # If it's already numpy, we pass it directly
        audio_data = np.array(audio_data)
    elif isinstance(audio_data, torch.Tensor):
        # If it's a tensor, convert to numpy array
        audio_data = audio_data.numpy()
    else:
        print("Error: Audio data is neither a numpy array nor a tensor.")
        return "Invalid audio format"

    # Now transcribe the audio
    return asr_model.transcribe([audio_data])

# Create the Gradio interface with audio input and text output
iface = gr.Interface(fn=transcribe, inputs="audio", outputs="text")

# Launch the Gradio interface
iface.launch()