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from transformers import pipeline | |
from transformers import WhisperForConditionalGeneration, WhisperProcessor | |
from transformers import WhisperTokenizer | |
from transformers import WhisperFeatureExtractor | |
import gradio as gr | |
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Spanish", task="transcribe") | |
model = WhisperForConditionalGeneration.from_pretrained("mirari/whisper-small-es") | |
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small") | |
pipe = pipeline(task="automatic-speech-recognition",model=model, tokenizer=tokenizer,feature_extractor=feature_extractor) | |
def transcribe(audio): | |
text = pipe(audio)["text"] | |
return text | |
iface = gr.Interface( | |
fn=transcribe, | |
inputs=gr.Audio(source="microphone", type="filepath"), | |
outputs="text", | |
title="Whisper Small Hindi", | |
description="Realtime demo for Spanish speech recognition using a fine-tuned Whisper small model.", | |
) | |
iface.launch() |