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import os
import argparse
from lang_list import LANGUAGE_NAME_TO_CODE, WHISPER_LANGUAGES
from tqdm import tqdm
# For pyannote.audio diarize
from pyannote.audio import Model
model = Model.from_pretrained("pyannote/segmentation-3.0", use_auth_token="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")
language_dict = {}
# Iterate over the LANGUAGE_NAME_TO_CODE dictionary
for language_name, language_code in LANGUAGE_NAME_TO_CODE.items():
# Extract the language code (the first two characters before the underscore)
lang_code = language_code.split('_')[0].lower()
# Check if the language code is present in WHISPER_LANGUAGES
if lang_code in WHISPER_LANGUAGES:
# Construct the entry for the resulting dictionary
language_dict[language_name] = {
"transcriber": lang_code,
"translator": language_code
}
def transcribe(audio_file, language, device, vocals):
output_folder = "transcriptions"
# Transcribe audio file
model = "large-v2"
# word_timestamps = True
print_progress = False
compute_type = "float16"
fp16 = True
batch_size = 8
verbose = False
min_speakers = 1
max_speakers = 10
threads = 4
output_format = "srt"
hf_token = "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"
command = f'whisperx {audio_file} --model {model} --batch_size {batch_size} --compute_type {compute_type} \
--output_dir {output_folder} --output_format {output_format} --verbose {verbose} --language {language} \
--fp16 {fp16} --threads {threads} --print_progress {print_progress} --device {device}'
if vocals:
command += f' --diarize --max_speakers {max_speakers} --min_speakers {min_speakers} --hf_token {hf_token}'
os.system(command)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Transcribe audio files')
parser.add_argument('input_files', help='Input audio files')
parser.add_argument('language', help='Language of the audio file')
parser.add_argument('speakers_file', help='File with the number of speakers')
parser.add_argument('device', help='Device to use for PyTorch inference')
parser.add_argument('vocals', help='Vocals or not')
args = parser.parse_args()
vocals_folder = "vocals"
with open(args.speakers_file, 'r') as f:
speakers = f.read().splitlines()
speakers = int(speakers[0])
with open(args.input_files, 'r') as f:
inputs = f.read().splitlines()
progress_bar = tqdm(total=len(inputs), desc="Transcribe audio files progress")
for input in inputs:
input_file, _ = input.split('.')
_, input_name = input_file.split('/')
if speakers > 0:
extension = "wav"
for i in range(speakers):
file = f'{vocals_folder}/{input_name}_speaker{i:003d}.{extension}'
transcribe(file, language_dict[args.language]["transcriber"], args.device, args.vocals)
else:
extension = "mp3"
file = f'{vocals_folder}/{input_name}.{extension}'
transcribe(file, language_dict[args.language]["transcriber"], args.device, args.vocals)
progress_bar.update(1)
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