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subtify / transcribe.py

Maximofn

Remove vocals from ui code

c7ef4ea 7 months ago

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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, num_speakers, device):
	output_folder = "transcriptions"

	# Transcribe audio file
	model = "large-v2"
	# word_timestamps = True
	print_progress = False
	if device == "cpu":
	# I supose that I am on huggingface server
	compute_type = "float32"
	else:
	compute_type = "float16"
	fp16 = True
	batch_size = 8
	verbose = False
	min_speakers = 1
	max_speakers = num_speakers
	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} \
	--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('num_speakers', help='Number of speakers in the audio file')
	parser.add_argument('device', help='Device to use for PyTorch inference')
	args = parser.parse_args()

	chunks_folder = "chunks"

	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('/')
	extension = "mp3"
	file = f'{chunks_folder}/{input_name}.{extension}'
	transcribe(file, language_dict[args.language]["transcriber"], args.num_speakers, args.device)
	progress_bar.update(1)