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""" |
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Script for extracting DeepSpeech features from audio file. |
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""" |
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import os |
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import argparse |
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import numpy as np |
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import pandas as pd |
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from deepspeech_store import get_deepspeech_model_file |
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from deepspeech_features import conv_audios_to_deepspeech |
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def parse_args(): |
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""" |
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Create python script parameters. |
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Returns |
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------- |
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ArgumentParser |
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Resulted args. |
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""" |
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parser = argparse.ArgumentParser( |
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description="Extract DeepSpeech features from audio file", |
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formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
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parser.add_argument( |
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"--input", |
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type=str, |
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required=True, |
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help="path to input audio file or directory") |
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parser.add_argument( |
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"--output", |
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type=str, |
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help="path to output file with DeepSpeech features") |
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parser.add_argument( |
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"--deepspeech", |
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type=str, |
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help="path to DeepSpeech 0.1.0 frozen model") |
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parser.add_argument( |
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"--metainfo", |
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type=str, |
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help="path to file with meta-information") |
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args = parser.parse_args() |
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return args |
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def extract_features(in_audios, |
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out_files, |
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deepspeech_pb_path, |
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metainfo_file_path=None): |
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""" |
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Real extract audio from video file. |
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Parameters |
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---------- |
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in_audios : list of str |
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Paths to input audio files. |
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out_files : list of str |
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Paths to output files with DeepSpeech features. |
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deepspeech_pb_path : str |
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Path to DeepSpeech 0.1.0 frozen model. |
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metainfo_file_path : str, default None |
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Path to file with meta-information. |
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""" |
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if metainfo_file_path is None: |
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num_frames_info = [None] * len(in_audios) |
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else: |
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train_df = pd.read_csv( |
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metainfo_file_path, |
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sep="\t", |
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index_col=False, |
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dtype={"Id": np.int, "File": np.unicode, "Count": np.int}) |
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num_frames_info = train_df["Count"].values |
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assert (len(num_frames_info) == len(in_audios)) |
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for i, in_audio in enumerate(in_audios): |
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if not out_files[i]: |
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file_stem, _ = os.path.splitext(in_audio) |
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out_files[i] = file_stem + "_ds.npy" |
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conv_audios_to_deepspeech( |
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audios=in_audios, |
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out_files=out_files, |
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num_frames_info=num_frames_info, |
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deepspeech_pb_path=deepspeech_pb_path) |
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def main(): |
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""" |
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Main body of script. |
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""" |
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args = parse_args() |
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in_audio = os.path.expanduser(args.input) |
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if not os.path.exists(in_audio): |
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raise Exception("Input file/directory doesn't exist: {}".format(in_audio)) |
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deepspeech_pb_path = args.deepspeech |
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deepspeech_pb_path = True |
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args.deepspeech = '~/.tensorflow/models/deepspeech-0_1_0-b90017e8.pb' |
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if deepspeech_pb_path is None: |
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deepspeech_pb_path = "" |
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if deepspeech_pb_path: |
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deepspeech_pb_path = os.path.expanduser(args.deepspeech) |
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if not os.path.exists(deepspeech_pb_path): |
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deepspeech_pb_path = get_deepspeech_model_file() |
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if os.path.isfile(in_audio): |
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extract_features( |
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in_audios=[in_audio], |
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out_files=[args.output], |
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deepspeech_pb_path=deepspeech_pb_path, |
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metainfo_file_path=args.metainfo) |
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else: |
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audio_file_paths = [] |
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for file_name in os.listdir(in_audio): |
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if not os.path.isfile(os.path.join(in_audio, file_name)): |
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continue |
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_, file_ext = os.path.splitext(file_name) |
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if file_ext.lower() == ".wav": |
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audio_file_path = os.path.join(in_audio, file_name) |
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audio_file_paths.append(audio_file_path) |
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audio_file_paths = sorted(audio_file_paths) |
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out_file_paths = [""] * len(audio_file_paths) |
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extract_features( |
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in_audios=audio_file_paths, |
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out_files=out_file_paths, |
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deepspeech_pb_path=deepspeech_pb_path, |
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metainfo_file_path=args.metainfo) |
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if __name__ == "__main__": |
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main() |
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