Replace WhisperX with faster-whisper

app.py

@@ -1,90 +1,33 @@
 import gradio as gr
-import whisperx
-import os
-import tempfile
-from dotenv import load_dotenv
-
-# Load environment variables
-load_dotenv()
-hf_token = os.getenv("hf_token")
+from faster_whisper import WhisperModelimport os
 
 # Model config
 device = "cpu"
-batch_size = 16
 compute_type = "int8"
-
 # Load main model
-model = whisperx.load_model("large-v3", device, compute_type=compute_type)
+model = WhisperModel("large-v3", device=device, compute_type=compute_type)
 
 title = "🎙️ Multilingual Audio Processor"
-description = "Upload an audio file and select whether to transcribe, align words, or identify speakers (Powered by WhisperX)."
-
-def clean_alignment(result):
-    cleaned_segments = []
-    for seg in result.get("segments", []):
-        cleaned_words = []
-        for word in seg.get("words", []):
-            cleaned_words.append({
-                "word": word["word"],
-                "start": float(word["start"]),
-                "end": float(word["end"]),
-                "score": float(word["score"])
-            })
-        cleaned_segments.append({
-            "text": seg["text"],
-            "start": float(seg["start"]),
-            "end": float(seg["end"]),
-            "words": cleaned_words
-        })
-    return {"segments": cleaned_segments}
-
-def process_audio(audio_path, transcribe=True, align=False, diarize=False):
-    transcript_output = ""
-    align_output = {}
-    diarize_output = ""
-
-    audio = whisperx.load_audio(audio_path)
-    result = None
-
-    # Step 1: Transcribe
-    # if transcribe:
-    result = model.transcribe(audio, batch_size=batch_size)
-    transcript_output = " ".join(seg["text"] for seg in result["segments"])
-
-    # Step 2: Align
-    if align and result:
-        model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
-        result = whisperx.align(result["segments"], model_a, metadata, audio, device)
-        align_output = clean_alignment(result)
-
-    # Step 3: Diarization
-    if diarize and result:
-        diarize_model = whisperx.diarize.DiarizationPipeline(
-            use_auth_token=hf_token,
-            device=device
-        )
-        diarize_segments = diarize_model(audio)
-        result = whisperx.assign_word_speakers(diarize_segments, result)
-        diarize_output = [
-            {
-                "start": float(seg["start"]),
-                "end": float(seg["end"]),
-                "speaker": seg.get("speaker", "SPEAKER_00"),
-                "text": seg["text"]
-            } for seg in result["segments"]
-            ]
-
-    return transcript_output , align_output or {}, diarize_output or "No diarization."
-
-with gr.Blocks(title=title, theme=gr.themes.Default(), analytics_enabled=True) as demo:
+description = "Upload an audio file to transcribe (Powered by faster-whisper)."
+
+def process_audio(audio_path):
+
+    # Transcribe using faster-whisper
+    segments, info = model.transcribe(audio_path)
+    
+    # Extract text from segments
+    transcript_output = " ".join([seg.text for seg in segments])
+    return transcript_output
+    
+    
+    
+    with gr.Blocks(title=title, theme=gr.themes.Default(), analytics_enabled=True) as demo:
     gr.Markdown(f"<h1 style='text-align: center;font-size: 40px;'>{title}</h1>")
     gr.Markdown(f"<p style='text-align: center; font-size: 16px;'>{description}</p>")
+    
     with gr.Row():
         with gr.Column(scale=1):
             audio_input = gr.Audio(type="filepath", label="Upload Audio")
-            transcribe_checkbox = gr.Markdown("✅ Transcription will always be performed.")
-            align_checkbox = gr.Checkbox(label="Align")
-            diarize_checkbox = gr.Checkbox(label="Diarize")
             gr.Markdown("### <span style='font-size: 18px;'>🎧 Try Sample Audio</span>")
             gr.Examples(
                 examples=[[f"test_audios/{audio_file}"] for audio_file in os.listdir("test_audios") if audio_file.endswith(('.mp3', '.wav'))],
@@ -93,15 +36,14 @@ with gr.Blocks(title=title, theme=gr.themes.Default(), analytics_enabled=True) a
             )
         with gr.Column(scale=2):
             transcript_output = gr.Textbox(label="📄 Transcript", lines=10, interactive=False)
-            alignment_output = gr.JSON(label="🧭 Word Alignment")
-            diarization_output = gr.JSON(label="🗣️ Speaker Diarization")
-    with gr.Row():
+            with gr.Row():
         process_button = gr.Button("Process")
 
+            process_button.click(
     process_button.click(
         fn=process_audio,
-        inputs=[audio_input, transcribe_checkbox, align_checkbox, diarize_checkbox],
-        outputs=[transcript_output, alignment_output, diarization_output]
+        inputs=[audio_input],
+        outputs=[transcript_output]
     )
 
 if __name__ == "__main__":