| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from peft import PeftModel, PeftConfig | |
| from transformers import WhisperForConditionalGeneration, WhisperTokenizer, WhisperProcessor, AutomaticSpeechRecognitionPipeline | |
| peft_model_id = "mfidabel/Modelo_1_Whisper_Large_V3" | |
| language = "guarani" | |
| task = "transcribe" | |
| peft_config = PeftConfig.from_pretrained(peft_model_id) | |
| model = WhisperForConditionalGeneration.from_pretrained( | |
| peft_config.base_model_name_or_path, load_in_8bit=True, device_map="auto" | |
| ) | |
| model = PeftModel.from_pretrained(model, peft_model_id) | |
| tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task) | |
| processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task) | |
| feature_extractor = processor.feature_extractor | |
| forced_decoder_ids = processor.get_decoder_prompt_ids(language="english", task=task) | |
| pipeline = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor) | |
| def transcribe(audio): | |
| if audio is None: | |
| return "Espera a que la grabación termine de subirse al servidor !! Intentelo de nuevo en unos segundos" | |
| sr, y = audio | |
| y = y.astype(np.float32) | |
| y /= np.max(np.abs(y)) | |
| with torch.cuda.amp.autocast(): | |
| return pipeline({"sampling_rate": sr, "raw": y}, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"] | |
| gr.Interface(fn=transcribe, inputs="microphone", outputs="text").launch(share=True) |