v2

app.py

@@ -22,43 +22,10 @@ if st.sidebar.button("Transkripsikan Audio"):
         with NamedTemporaryFile() as temp:
             temp.write(audio_file.getvalue())
             temp.seek(0)
-            model = whisper.load_model("large")
+            model = whisper.load_model("large-v2")
             result = model.transcribe(temp.name)
             st.write(result["text"])
         
 st.sidebar.header("Putar Berkas Audio")
 st.sidebar.audio(audio_file)
 
-# import streamlit as st
-# from tempfile import NamedTemporaryFile
-# import ffmpeg
-# from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
-# import librosa
-
-# # HF_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
-
-# st.title("TemplarX-Medium-Indonesian Transcription App")
-# st.text("Model Whisper (TemplarX-medium-Indonesian) telah dimuat:")
-
-# def load_whisper_model():
-#     model_name = "jonnatakusuma/TemplarX-medium-Indonesian"
-#     tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)
-#     model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=True)
-#     return tokenizer, model
-
-# audio_file = st.file_uploader("Unggah Meeting Audio", type=["mp3", "wav", "m4a"])
-
-# if st.sidebar.button("Transkripsikan Audio"):
-#     if audio_file is not None:
-#         with NamedTemporaryFile() as temp:
-#             temp.write(audio_file.read())
-#             temp.seek(0)
-#             tokenizer, model = load_whisper_model()
-#             # Read the audio file and transcribe using the fine-tuned model
-#             audio_path = temp.name
-#             audio_input, _ = librosa.load(audio_path, sr=16000)
-#             transcription = model.stt(text)
-#             st.write(transcription)
-
-# st.sidebar.header("Putar Berkas Audio")
-# st.sidebar.audio(audio_file, format='audio/wav')