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| import torch | |
| import transformers | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
| import requests | |
| import uuid | |
| WAV2VEC = "wav2vec" | |
| AUTOMODELFORSPEECH = "automodelforspeech" | |
| class Audio_to_Text: | |
| def __init__(self): | |
| self.model_id = "openai/whisper-large-v3" | |
| self.device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| self.torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| self.model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
| self.model_id, torch_dtype=self.torch_dtype, low_cpu_mem_usage=True, use_safetensors=True | |
| ) | |
| self.model.to(self.device) | |
| self.processor = AutoProcessor.from_pretrained(self.model_id) | |
| self.pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model=self.model, | |
| tokenizer=self.processor.tokenizer, | |
| feature_extractor=self.processor.feature_extractor, | |
| max_new_tokens=128, | |
| chunk_length_s=30, | |
| batch_size=16, | |
| return_timestamps=True, | |
| torch_dtype=self.torch_dtype, | |
| device=self.device, | |
| ) | |
| print("Model loaded successfully.") | |
| def download_mp3(self, url, save_path): | |
| response = requests.get(url) | |
| with open(save_path, 'wb') as file: | |
| file.write(response.content) | |
| print("MP3 file downloaded and saved successfully.") | |
| def convert_audio_to_text(self, audio_file, transcription_method): | |
| if transcription_method == WAV2VEC: | |
| return self.transcribe_audio_to_text_using_wav2vec(audio_file) | |
| else: | |
| transformers.logging.set_verbosity_info() | |
| result = self.pipe(audio_file, generate_kwargs={"language": "english"}) | |
| print("Converted audio to text successfully.") | |
| # save the result to a text file | |
| return self.save_transcribed_text_to_file(result) | |
| def convert_audio_to_text_from_url(self, url, transcription_method): | |
| #get uuid for the audio file | |
| uuid_audio = str(uuid.uuid4()) | |
| save_path = f"audio-{uuid_audio}.mp3" | |
| self.download_mp3(url, save_path) | |
| path_text_file_of_audio = self.convert_audio_to_text(save_path) | |
| return path_text_file_of_audio | |
| def save_transcribed_text_to_file(self, text): | |
| uuid_text = str(uuid.uuid4()) | |
| save_file_name = f"transcript-{uuid_text}.txt" | |
| with open(save_file_name, "w") as file: | |
| file.write(text) | |
| print("Transcript saved successfully.") | |
| return save_file_name | |
| def transcribe_audio_to_text_using_wav2vec(self, mp3): | |
| asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") | |
| text = asr(mp3)["text"] | |
| return self.save_transcribed_text_to_file(text) | |
| def transcribe_podcast_from_mp3(mp3_file, transcription_method): | |
| audio_to_text = Audio_to_Text() | |
| return audio_to_text.convert_audio_to_text(mp3_file, transcription_method); | |
| def transcribe_podcast(file_url, transcription_method): | |
| # Example usage: | |
| # url = "https://chrt.fm/track/138C95/prfx.byspotify.com/e/play.podtrac.com/npr-510310/traffic.megaphone.fm/NPR7010771664.mp3" | |
| audio_to_text = Audio_to_Text() | |
| # Convert the audio file to text | |
| path_text_file_of_audio = audio_to_text.convert_audio_to_text_from_url(file_url, transcription_method) | |
| # Print the result | |
| print(path_text_file_of_audio) | |
| return path_text_file_of_audio |