fine tune

app.py

@@ -1,31 +1,62 @@
-import streamlit as st
-import whisper
-from tempfile import NamedTemporaryFile
-import ffmpeg
+# import streamlit as st
+# import whisper
+# from tempfile import NamedTemporaryFile
+# import ffmpeg
 
 
-st.title("MinuteBot App")
+# st.title("MinuteBot App")
 
-# upload audio file with streamlit
-audio_file = st.file_uploader("Unggah Meeting Audio", type=["mp3", "wav", "m4a"])
+# # upload audio file with streamlit
+# audio_file = st.file_uploader("Unggah Meeting Audio", type=["mp3", "wav", "m4a"])
 
-# model = whisper.load_model("base") # loading the base model
-st.text("MinuteBot Model telah dimuat:")
+# # model = whisper.load_model("base") # loading the base model
+# st.text("MinuteBot Model telah dimuat:")
 
-def load_whisper_model():
+# def load_whisper_model():
 
-    return model
+#     return model
 
     
+# if st.sidebar.button("Transkripsikan Audio"):
+#     if audio_file is not None:
+#         with NamedTemporaryFile() as temp:
+#             temp.write(audio_file.getvalue())
+#             temp.seek(0)
+#             model = whisper.load_model("large")
+#             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
+
+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)
+    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.getvalue())
+            temp.write(audio_file.read())
             temp.seek(0)
-            model = whisper.load_model("large")
-            result = model.transcribe(temp.name)
-            st.write(result["text"])
-        
-st.sidebar.header("Putar Berkas Audio")
-st.sidebar.audio(audio_file)
+            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')