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| # Convert video .mkv em audio .mp3 16khz | |
| # ffmpeg -i video.mkv -acodec libmp3lame -ac 1 -ar 16000 output.mp3 | |
| # | |
| # Quebra o audio .mp3 em pedacos de 30 segundos cada | |
| # ffmpeg -i output.mp3 -f segment -segment_time 30 -c copy samples/sample%05d.mp3 | |
| from huggingsound import SpeechRecognitionModel, KenshoLMDecoder | |
| import torch | |
| import os | |
| def listar_arquivos_mp3(diretorio): | |
| arquivos = os.listdir(diretorio) | |
| arquivos_mp3 = [os.path.join(diretorio, arquivo) for arquivo in arquivos if arquivo.endswith('.mp3')] | |
| arquivos_mp3.sort() | |
| return arquivos_mp3 | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese", device=device) | |
| input_files = listar_arquivos_mp3('./samples') | |
| # The LM format used by the LM decoders is the KenLM format (arpa or binary file). | |
| # You can download some LM files examples from here: https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese/tree/main/language_model | |
| # lm_path = "lm_files/lm.binary" | |
| # unigrams_path = "lm_files/unigrams.txt" | |
| # We implemented three different decoders for LM boosted decoding: KenshoLMDecoder, ParlanceLMDecoder, and FlashlightLMDecoder | |
| # On this example, we'll use the KenshoLMDecoder | |
| # To use this decoder you'll need to install the Kensho's ctcdecode first (https://github.com/kensho-technologies/pyctcdecode) | |
| # decoder = KenshoLMDecoder(model.token_set, lm_path=lm_path, unigrams_path=unigrams_path) | |
| # transcription = model.transcribe(input_files, decoder=decoder) | |
| transcription = model.transcribe(input_files) | |
| with open('transcription.txt', 'w', encoding='utf-8') as f: | |
| for i in transcription: | |
| text = i['transcription'] | |
| f.write(text) | |
| f.write('\n') | |
| # print(transcription) |
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