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