run whisper locally

app.py

@@ -1,5 +1,5 @@
 from flask import Flask, render_template, request
-# import whisper
+import whisper
 import tempfile
 import os
 import time
@@ -7,14 +7,14 @@ import torch
 import numpy as np
 import requests
 from tqdm import tqdm
-from transformers import BertTokenizer, AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from transformers import BertTokenizer
 from model.multi_class_model import MultiClassModel  # Adjust if needed
+import lightning as L
 
 app = Flask(__name__)
 
 # === CONFIG ===
-# CHECKPOINT_URL = "https://github.com/michael2002porto/bert_classification_indonesian_song_lyrics/releases/download/finetuned_checkpoints/original_split_synthesized.ckpt"
-CHECKPOINT_URL = "https://huggingface.co/nenafem/original_split_synthesized/resolve/main/original_split_synthesized.ckpt?download=true"
+CHECKPOINT_URL = "https://github.com/michael2002porto/bert_classification_indonesian_song_lyrics/releases/download/finetuned_checkpoints/original_split_synthesized.ckpt"
 CHECKPOINT_PATH = "final_checkpoint/original_split_synthesized.ckpt"
 AGE_LABELS = ["semua usia", "anak", "remaja", "dewasa"]
 
@@ -50,36 +50,6 @@ model = MultiClassModel.load_from_checkpoint(
 model.eval()
 
 
-def whisper_api(temp_audio_path):
-    # https://huggingface.co/openai/whisper-large-v3
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-    model_id = "openai/whisper-large-v3"
-
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-    )
-    model.to(device)
-
-    processor = AutoProcessor.from_pretrained(model_id)
-
-    pipe = pipeline(
-        "automatic-speech-recognition",
-        model=model,
-        tokenizer=processor.tokenizer,
-        feature_extractor=processor.feature_extractor,
-        chunk_length_s=30,
-        batch_size=32,  # batch size for inference - set based on your device
-        torch_dtype=torch_dtype,
-        device=device,
-    )
-
-    result = pipe(temp_audio_path, return_timestamps=False, generate_kwargs={"language": "indonesian"})
-    print(result["text"])
-    return result
-
-
 # === ROUTES ===
 
 @app.route('/', methods=['GET'])
@@ -92,7 +62,7 @@ def transcribe():
     try:
         # Load Whisper with Indonesian language support (large / turbo)
         # https://github.com/openai/whisper
-        # whisper_model = whisper.load_model("large")
+        whisper_model = whisper.load_model("large")
 
         # Start measuring time
         start_time = time.time()
@@ -105,8 +75,7 @@ def transcribe():
                 temp_audio_path = temp_audio.name
 
             # Step 1: Transcribe
-            # transcription = whisper_model.transcribe(temp_audio_path, language="id")
-            transcription = whisper_api(temp_audio_path)
+            transcription = whisper_model.transcribe(temp_audio_path, language="id")
             os.remove(temp_audio_path)
             transcribed_text = transcription["text"]
 

requirements.txt

@@ -7,8 +7,8 @@ Jinja2==2.11.3
 MarkupSafe==1.1.1
 SQLAlchemy==1.3.22
 Werkzeug==1.0.1
-# openai-whisper
-# setuptools-rust
+openai-whisper
+setuptools-rust
 # ffmpeg
 # ffmpeg-python
 # imageio[ffmpeg]