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"""
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.
"""
#deepspeech_pb_path="/disk4/keyu/DeepSpeech/deepspeech-0.9.2-models.pbmm"
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 + ".npy"
#print(out_files[i])
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
#add
deepspeech_pb_path = True
args.deepspeech = '~/.tensorflow/models/deepspeech-0_1_0-b90017e8.pb'
#deepspeech_pb_path="/disk4/keyu/DeepSpeech/deepspeech-0.9.2-models.pbmm"
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()
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