app.py

import gradio as gr
from transformers import pipeline
import torch
import librosa
import soundfile

SAMPLE_RATE = 16000

pipe = pipeline(model="birgermoell/whisper-small-sv-bm")  # change to "your-username/the-name-you-picked"


def process_audio_file(file):
    data, sr = librosa.load(file)

    if sr != SAMPLE_RATE:
        data = librosa.resample(data, sr, SAMPLE_RATE)

    # monochannel
    data = librosa.to_mono(data)
    return data


def transcribe(Microphone, File_Upload):
    warn_output = ""
    if (Microphone is not None) and (File_Upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        file = Microphone

    elif (Microphone is None) and (File_Upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"

    elif Microphone is not None:
        file = Microphone
    else:
        file = File_Upload

    audio_data = process_audio_file(file)
    
    text = pipe(audio_data)["text"]

    return warn_output + text


iface = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
        gr.inputs.Audio(source="upload", type='filepath', optional=True),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Whisper Small SV",
    description="Demo for Swedish speech recognition using the [Whisper Small SV BM checkpoint](https://huggingface.co/birgermoell/whisper-small-sv-bm).",
    allow_flagging='never',
)

iface.launch(enable_queue=True)