Update app.py

app.py

@@ -1,41 +1,53 @@
 import gradio as gr
 import torch
 import librosa
+import json
 from transformers import pipeline
 from stitched_model import CombinedModel
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-# Load the model
-model = CombinedModel("facebook/mms-1b-all", "Sunbird/sunbird-mul-en-mbart-merged", device=device)
+model = CombinedModel("indonesian-nlp/wav2vec2-luganda", "Sunbird/sunbird-mul-en-mbart-merged", device=device)
+
+def transcribe(audio_file_mic=None, audio_file_upload=None):
+    if audio_file_mic:
+        audio_file = audio_file_mic
+    elif audio_file_upload:
+        audio_file = audio_file_upload
+    else:
+        return "Please upload an audio file or record one"
 
-def transcribe(audio_file):
     # Load the audio file
     speech, sample_rate = librosa.load(audio_file, sr=16000, mono=True)
 
-    # Split the audio clip into smaller chunks
-    chunk_size =  30 * 16000
-    chunks = librosa.util.chunk(speech, chunk_size)
+    # Split the audio into 30-second chunks
+    chunk_size = 30 * 16000
+    chunks = [speech[i:i + chunk_size] for i in range(0, len(speech), chunk_size)]
 
-    # Transcribe each chunk individually
+    # Process each chunk and concatenate the results
     transcriptions = []
+    translations = []
     for chunk in chunks:
-        speech_tensor = torch.tensor([chunk])
+        chunk = torch.tensor([chunk])
         with torch.no_grad():
-            transcription = model({"audio": speech_tensor})["text"]
+            transcription, translation = model({"audio": chunk})
         transcriptions.append(transcription)
+        translations.append(translation[0])
 
-    # Join the transcriptions together
-    transcription = " ".join(transcriptions)
+    transcription = "".join(transcriptions)
+    translation = "".join(translations)
 
-    return transcription
+    return transcription, translation
 
 description = '''Luganda to English Speech Translation'''
 
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=[gr.Audio(source="upload", type="filepath", label="Upload Audio")],
-    outputs=[gr.Textbox(label="Transcription")],
-    description=description
-)
+iface = gr.Interface(fn=transcribe,
+                     inputs=[
+                         gr.Audio(source="microphone", type="filepath", label="Record Audio"),
+                         gr.Audio(source="upload", type="filepath", label="Upload Audio")],
+                     outputs=[gr.Textbox(label="Transcription"),
+                              gr.Textbox(label="Translation")
+                     ],
+                     description=description
+                     )
 iface.launch()