Create app.py

app.py

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+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)