app.py

import gradio as gr
from pyannote.audio import Pipeline
from datasets import load_dataset
from transformers import pipeline

librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
asr = pipeline(
    "automatic-speech-recognition",
    model="facebook/s2t-wav2vec2-large-en-de",
    feature_extractor="facebook/s2t-wav2vec2-large-en-de",
)

def speech_to_text(audio):
    translation = asr(librispeech_en[0][audio])
    return translation

def diarization(audio):
    pipeline = Pipeline.from_pretrained("pyannote/speaker-segmentation")
    output = pipeline(audio)
    result = ""
    for turn, _, speaker in output.itertracks(yield_label=True):
        text_result = speech_to_text(audio)
        result += "{} said '{}' from {:.3f} to {:.3f}\n".format(speaker,text_result,turn.start,turn.end)
    return "No output" if result == "" else result

title = "Speech Recognition with Speaker Diarization"
description = "Speaker Diarization is the act of attributing parts of the audio recording to different speakers. This space aims to distinguish the speakers and apply speech-to-text from a given input audio file. Pre-trained models from Pyannote[1] for the Speaker Diarization and [2]."
article = "<p style='text-align: center'><a href='https://github.com/pyannote/pyannote-audio' target='_blank'>[1] Pyannote - Speaker Diarization model</a></p>"

app = gr.Interface(fn=diarization,
                inputs=gr.inputs.Audio(source="upload", type="filepath", label="Upload your audio file here:"),
                outputs=gr.outputs.Textbox(type="auto", label="OUTPUT"),
                examples=[["test_audio1.wav"]],
                title=title,
                description=description,
                article=article,
                allow_flagging=False)
app.launch(enable_queue=True)