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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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import wave |
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import subprocess |
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import sys |
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import shlex |
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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 convert_samplerate(audio_path, desired_sample_rate): |
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sox_cmd = 'sox {} --type raw --bits 16 --channels 1 --rate {} --encoding signed-integer --endian little --compression 0.0 --no-dither - '.format(quote(audio_path), desired_sample_rate) |
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try: |
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output = subprocess.check_output(shlex.split(sox_cmd), stderr=subprocess.PIPE) |
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except subprocess.CalledProcessError as e: |
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raise RuntimeError('SoX returned non-zero status: {}'.format(e.stderr)) |
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except OSError as e: |
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raise OSError(e.errno, 'SoX not found, use {}hz files or install it: {}'.format(desired_sample_rate, e.strerror)) |
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return desired_sample_rate, np.frombuffer(output, np.int16) |
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def transcribe(audio_file): |
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desired_sample_rate = model.sampleRate() |
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fin = wave.open(audio_file, 'rb') |
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fs_orig = fin.getframerate() |
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if fs_orig != desired_sample_rate: |
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print('Warning: original sample rate ({}) is different than {}hz. Resampling might produce erratic speech recognition.'.format(fs_orig, desired_sample_rate), file=sys.stderr) |
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fs_new, audio = convert_samplerate(audio_file, desired_sample_rate) |
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else: |
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audio = np.frombuffer(fin.readframes(fin.getnframes()), np.int16) |
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audio_length = fin.getnframes() * (1/fs_orig) |
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fin.close() |
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text = model.stt(audio) |
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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() |
