Add Moroccan Darija extraction app4

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import torch
 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, pipeline
 import soundfile as sf
+import librosa
 
 # Load models
 # Transcription model for Moroccan Darija
@@ -9,40 +10,42 @@ processor = Wav2Vec2Processor.from_pretrained("boumehdi/wav2vec2-large-xlsr-moro
 transcription_model = Wav2Vec2ForCTC.from_pretrained("boumehdi/wav2vec2-large-xlsr-moroccan-darija")
 
 # Summarization model
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+summarizer = pipeline("summarization", model="t5-small")
 
-# Function to transcribe audio
+# Function to transcribe audio using Wav2Vec2
 def transcribe_audio(audio_path):
-    audio_input, sample_rate = sf.read(audio_path)
-    if sample_rate != 16000:
-        raise ValueError("Audio must be sampled at 16kHz.")
-    inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt", padding=True)
+    # Load and resample audio to 16kHz
+    audio_input, original_sample_rate = sf.read(audio_path)
+    if original_sample_rate != 16000:
+        audio_input = librosa.resample(audio_input, orig_sr=original_sample_rate, target_sr=16000)
+
+    # Process audio for transcription
+    inputs = processor(audio_input, sampling_rate=16000, return_tensors="pt", padding=True)
     with torch.no_grad():
         logits = transcription_model(**inputs).logits
     predicted_ids = torch.argmax(logits, dim=-1)
     transcription = processor.batch_decode(predicted_ids)[0]
     return transcription
 
-# Function to analyze topics from summary
+# Function to analyze topics
 def analyze_topics(summary):
     if "customer service" in summary.lower():
         return "Customer Service"
     elif "retention" in summary.lower():
         return "Retention"
     else:
-        return "Unknown"
+        return "Other"
 
-# Function to transcribe, summarize, and analyze topics
+# Function to transcribe, summarize, and analyze
 def transcribe_summarize_analyze(audio_file):
     # Transcription
     transcription = transcribe_audio(audio_file)
 
     # Summarization
-    summary = summarizer(transcription, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
+    summary = summarizer(transcription, max_length=50, min_length=10, do_sample=False)[0]["summary_text"]
 
     # Topic Analysis
     topic = analyze_topics(summary)
-
     return transcription, summary, topic
 
 # Gradio Interface
@@ -57,11 +60,8 @@ app = gr.Interface(
     fn=transcribe_summarize_analyze,
     inputs=inputs,
     outputs=outputs,
-    title="Moroccan Darija Audio Analysis",
-    description=(
-        "Upload an audio file in Moroccan Darija to get its transcription, a summarized version, "
-        "and the detected topic (Customer Service or Retention)."
-    )
+    title="Moroccan Darija Audio Processing",
+    description="Upload an audio file in Moroccan Darija to get its transcription, a summarized version of the content, and an identified topic (e.g., Customer Service or Retention)."
 )
 
 # Launch the app