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Create app.py
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app.py
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import gradio as gr
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import soundfile as sf
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import numpy as np
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import tempfile
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import torchaudio
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from transformers import AutoModel
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# Load ASR Model
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def load_model():
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return AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
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model = load_model()
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def process_audio(audio, language, decoding_method):
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if isinstance(audio, tuple): # Recorded audio
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sample_rate, data = audio
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temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
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sf.write(temp_wav.name, data, sample_rate)
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audio_path = temp_wav.name
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else: # Uploaded file
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audio_path = audio
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# Load and resample audio
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wav, sr = torchaudio.load(audio_path)
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target_sample_rate = 16000
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if sr != target_sample_rate:
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resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate)
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wav = resampler(wav)
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# Perform ASR with selected decoding method
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transcription = model(wav, language, decoding_method)
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return transcription
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iface = gr.Interface(
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fn=process_audio,
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inputs=[
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gr.Audio(source="microphone", type="numpy"),
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gr.Audio(source="upload"),
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gr.Dropdown(["hi", "ta", "bn", "mr", "te", "gu", "kn", "ml", "pa", "ur"], label="Select Language"),
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gr.Radio(["ctc", "rnnt"], label="Decoding Method")
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],
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outputs="text",
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title="Multilingual ASR with Indic-Conformer",
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description="Record or upload an audio file, select a language and decoding method, and transcribe it using the AI4Bharat Indic-Conformer model."
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)
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if __name__ == "__main__":
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iface.launch()
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