audio.py

import torch
import gradio as gr
from pathlib import Path
from transformers import pipeline

model_id = "Sandiago21/whisper-large-v2-spanish"

cache_path = Path("~/.cache/huggingface/transformers") / model_id
if not cache_path.is_dir():
    pipe = pipeline("automatic-speech-recognition", model=model_id)
else:
    pipe = pipeline("automatic-speech-recognition", model=cache_path)

def transcribe_speech(filepath):
    output = pipe(
        filepath,
        max_new_tokens=256,
        generate_kwargs={
            "task": "transcribe",
            "language": "spanish",
        },  
        chunk_length_s=30,
        batch_size=8,
    )
    return output["text"]

demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=gr.outputs.Textbox(),

)

file_transcribe = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs=gr.outputs.Textbox(),

)

with demo:
    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Transcribe Microphone", "Transcribe Audio File"],
    ),

demo.launch()