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