Create app.py

app.py

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+
+from transformers import (
+    AutomaticSpeechRecognitionPipeline,
+    WhisperForConditionalGeneration,
+    WhisperTokenizer,
+    WhisperProcessor,
+)
+from peft import PeftModel, PeftConfig
+import torch
+from huggingface_hub import snapshot_download
+
+peft_model_id = "aisha-org/faster-whisper-uz"
+language = "uz"
+task = "transcribe"
+
+peft_config = PeftConfig.from_pretrained(peft_model_id, use_auth_token=True)
+
+model = WhisperForConditionalGeneration.from_pretrained(
+    peft_config.base_model_name_or_path, 
+    load_in_8bit=True, 
+    device_map="auto",
+    use_auth_token=True,
+    force_download=True,
+    resume_download=False
+)
+
+model = PeftModel.from_pretrained(model, peft_model_id, use_auth_token=True)
+
+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
+import gradio as gr
+
+demo = gr.Blocks()
+
+mic_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources="microphone", type="filepath"),
+    outputs=gr.Textbox(),
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources="upload", type="filepath"),
+    outputs=gr.Textbox(),
+)
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe, file_transcribe],
+        ["Transcribe Microphone", "Transcribe Audio File"],
+    )
+
+demo.launch(debug=True)