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import soundfile as sf |
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import torch |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import gradio as gr |
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import scipy.signal as sps |
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import sox |
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def convert(inputfile, outfile): |
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sox_tfm = sox.Transformer() |
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sox_tfm.set_output_format( |
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file_type="wav", channels=1, encoding="signed-integer", rate=16000, bits=16 |
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) |
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sox_tfm.build(inputfile, outfile) |
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def read_file(wav): |
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sample_rate, signal = wav |
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signal = signal.mean(-1) |
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number_of_samples = round(len(signal) * float(16000) / sample_rate) |
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resampled_signal = sps.resample(signal, number_of_samples) |
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return resampled_signal |
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def parse_transcription(wav_file): |
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''' |
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filename = wav_file.name.split('.')[0] |
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convert(wav_file.name, filename + "16k.wav") |
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speech, _ = sf.read(filename + "16k.wav") |
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''' |
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speech = read_file(wav_file) |
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input_values = processor(speech, sampling_rate=16_000, return_tensors="pt").input_values |
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logits = model(input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) |
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return transcription |
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processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") |
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model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") |
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processor = Wav2Vec2Processor.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") |
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model = Wav2Vec2ForCTC.from_pretrained("Harveenchadha/vakyansh-wav2vec2-hindi-him-4200") |
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input_ = gr.inputs.Audio(source="microphone", type="numpy") |
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gr.Interface(parse_transcription, inputs = input_, outputs="text", |
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analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False); |
