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2a93225952
* changes * fix * fix for vars too * changes * fix tests Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
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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): # Deepspeech only supports 16k, (we convert 48k -> 16k)
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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(speech, stream):
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_, y = reformat_freq(*speech)
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if stream is None:
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stream = model.createStream()
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stream.feedAudioContent(y)
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text = stream.intermediateDecode()
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return text, stream
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demo = gr.Interface(
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transcribe,
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[gr.Audio(source="microphone", streaming=True), "state"],
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["text", "state"],
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live=True,
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)
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if __name__ == "__main__":
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demo.launch()
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