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import whisper | |
import gradio as gr | |
from transformers import pipeline | |
model = whisper.load_model("base") | |
sentiment_analysis = pipeline("sentiment-analysis",model="siebert/sentiment-roberta-large-english") | |
def process_audio_file(file): | |
with open(file, "rb") as f: | |
inputs = f.read() | |
audio = ffmpeg_read(inputs, sampling_rate) | |
return audio | |
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 | |
result = model.transcribe(file, task="translate") | |
return sentiment_analysis(result['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=[ | |
gr.outputs.Textbox(label="Language"), | |
gr.Number(label="Probability"), | |
], | |
layout="horizontal", | |
theme="huggingface", | |
title="Whisper Language Identification", | |
description="Demo for Language Identification using OpenAI's [Whisper Large V2](https://huggingface.co/openai/whisper-large-v2).", | |
allow_flagging='never', | |
) | |
iface.launch(enable_queue=True) |