build app

app.py

@@ -0,0 +1,42 @@
+import torch
+import gradio as gr
+from transformers import (
+    AutomaticSpeechRecognitionPipeline,
+    WhisperForConditionalGeneration,
+    WhisperTokenizer,
+    WhisperProcessor,
+)
+from peft import PeftModel, PeftConfig
+
+
+peft_model_id = "Namkoy/whisper_peft_vi_nam"
+language = "vietnamese"
+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=language, task=task)
+pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+
+def transcribe(audio):
+    with torch.cuda.amp.autocast():
+        text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
+    return text
+
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="PEFT LoRA + INT8 Whisper Large V2 Vietnamese",
+    description="Realtime demo for Vietnamese speech recognition using `PEFT-LoRA+INT8` fine-tuned Whisper Large V2 model.",
+)
+
+iface.launch(share=True)