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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")
os.system("pip install soundfile")
os.system("pip install datasets[audio]")
from evaluate import evaluator
from datasets import load_dataset, Audio
p = pipeline("automatic-speech-recognition")
task_evaluator = evaluator("automatic-speech-recognition")
#url = {"test" : "https://huggingface.co/datasets/mskov/miso_test/blob/main/test_set.parquet"}
data = load_dataset("audiofolder", data_dir="mskov/miso_test")
results = task_evaluator.compute(
model_or_pipeline="https://huggingface.co/mskov/whisper_miso",
data=data,
input_column="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()
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