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import gradio as gr
import librosa
from transformers import AutoFeatureExtractor, pipeline
def load_and_fix_data(input_file, model_sampling_rate):
speech, sample_rate = librosa.load(input_file)
if len(speech.shape) > 1:
speech = speech[:, 0] + speech[:, 1]
if sample_rate != model_sampling_rate:
speech = librosa.resample(speech, sample_rate, model_sampling_rate)
return speech
feature_extractor = AutoFeatureExtractor.from_pretrained(
"anuragshas/wav2vec2-xls-r-1b-hi-with-lm"
)
sampling_rate = feature_extractor.sampling_rate
asr = pipeline(
"automatic-speech-recognition", model="anuragshas/wav2vec2-xls-r-1b-hi-with-lm"
)
def predict_and_ctc_lm_decode(input_file):
speech = load_and_fix_data(input_file, sampling_rate)
transcribed_text = asr(speech, chunk_length_s=5, stride_length_s=1)
return transcribed_text["text"]
gr.Interface(
predict_and_ctc_lm_decode,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", label="Record your audio")
],
outputs=[gr.outputs.Textbox()],
examples=[["example1.wav"]],
title="Hindi ASR using Wav2Vec2-1B with LM",
article="<p><center><img src='https://visitor-badge.glitch.me/badge?page_id=anuragshas/Hindi_ASR' alt='visitor badge'></center></p>",
description="Built during Robust Speech Event",
layout="horizontal",
theme="huggingface",
).launch(enable_queue=True, cache_examples=True)
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