app.py

import transformers
from transformers import pipeline
import gradio as gr
import os 
import sys 
os.system("pip install evaluate")
os.system("pip install datasets")
os.system("pip install spicy")
from evaluate import evaluator
from datasets import load_dataset


p = pipeline("automatic-speech-recognition")

task_evaluator = evaluator("automatic-speech-recognition")
url = {"test" : "mskov/miso_test/test_set.parquet"}
data = load_dataset("parquet", data_files=url)
results = task_evaluator.compute(
    model_or_pipeline="https://huggingface.co/mskov/whisper_miso",
    data=data,
    input_column=["filepath","file_name"]
    label_column="category",
    metric="wer",
)
print(results)


def transcribe(audio, state=""):
    text = p(audio)["text"]
    state += text + " "
    return state, state

gr.Interface(
    fn=transcribe, 
    inputs=[
        gr.Audio(source="microphone", type="filepath", streaming=True), 
        "state"
    ],
    outputs=[
        "textbox",
        "state"
    ],
    live=True).launch()