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from deepspeech import Model |
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import gradio as gr |
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import numpy as np |
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import urllib.request |
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model_file_path = "deepspeech-0.9.3-models.pbmm" |
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lm_file_path = "deepspeech-0.9.3-models.scorer" |
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url = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/" |
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urllib.request.urlretrieve(url + model_file_path, filename=model_file_path) |
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urllib.request.urlretrieve(url + lm_file_path, filename=lm_file_path) |
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beam_width = 100 |
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lm_alpha = 0.93 |
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lm_beta = 1.18 |
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model = Model(model_file_path) |
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model.enableExternalScorer(lm_file_path) |
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model.setScorerAlphaBeta(lm_alpha, lm_beta) |
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model.setBeamWidth(beam_width) |
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def reformat_freq(sr, y): |
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if sr not in ( |
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48000, |
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16000, |
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): |
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raise ValueError("Unsupported rate", sr) |
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if sr == 48000: |
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y = ( |
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((y / max(np.max(y), 1)) * 32767) |
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.reshape((-1, 3)) |
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.mean(axis=1) |
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.astype("int16") |
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) |
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sr = 16000 |
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return sr, y |
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def transcribe(audio_file): |
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text = model.stt(audio_file) |
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return text |
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demo = gr.Interface( |
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transcribe, |
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gr.Audio(label="Upload Audio File", source="upload", type="filepath"), |
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outputs=gr.Textbox(label="Transcript") |
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) |
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if __name__ == "__main__": |
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demo.launch() |
