Update app.py

app.py

@@ -1,166 +1,19 @@
-import spaces
-import gradio as gr
-import subprocess
-import sys
 import os
+os.environ["TRANSFORMERS_NO_TF"] = "1"
+from transformers import pipeline
+import gradio as gr
 
-@spaces.GPU
-def transcribe(audio_file):
-    try:
-        # Load audio file
-        waveform, sample_rate = torchaudio.load(audio_file)
-        
-        # Move waveform to the correct device
-        waveform = waveform.to(device)
-
-        # Get the duration of the audio
-        duration = waveform.shape[1] / sample_rate
-
-        # Check if the audio is too short or too long
-        if duration < MIN_LENGTH or duration > MAX_LENGTH:
-            return f"Audio duration is too short or too long. Duration: {duration} seconds"
-
-        # Resample if necessary
-        if sample_rate != 16000:
-            resampler = torchaudio.transforms.Resample(sample_rate, 16000).to(device)
-            waveform = resampler(waveform)
-        
-        # Convert to mono if stereo
-        if waveform.shape[0] > 1:
-            waveform = waveform.mean(dim=0, keepdim=True)
-        
-        # Move to CPU for numpy conversion
-        waveform = waveform.cpu()
-        audio_input = waveform.squeeze().numpy()
-        
-        # Ensure audio input is float32
-        if audio_input.dtype != np.float32:
-            audio_input = audio_input.astype(np.float32)
-        
-        # Process audio input
-        input_values = processor(
-            audio_input, 
-            sampling_rate=16_000, 
-            return_tensors="pt"
-        ).input_values.to(device)
-        
-        # Convert to float16 if using CUDA
-        if torch_dtype == torch.float16:
-            input_values = input_values.half()
-
-        # Generate transcription
-        with torch.no_grad():
-            logits = model(input_values).logits
-
-        # Use language model for decoding
-        transcription = processor.decode(logits[0].cpu().numpy())
-        
-        # Return the transcription in lowercase
-        print(transcription)
-        return transcription[0].lower()
-    
-    except Exception as e:
-        return f"Error during transcription: {str(e)}"
-
-# Create Gradio interface
-
-css = """
-.textbox1 textarea {
-    font-size: 18px !important;
-    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
-    line-height: 1.8 !important;
-}
-.textbox2 textarea {
-    display: none;
-}
-"""
+pipe = pipeline(model="Devion333/wav2vec2-xls-r-300m-dv")
 
-demo = gr.Blocks(css=css)
+def transcribe(audio):
+  text = pipe(audio)["text"]
+  return text
 
-tab_audio = gr.Interface(
+demo = gr.Interface(
     fn=transcribe,
-    inputs=[
-        gr.Audio(sources=["upload","microphone"], type="filepath", label="Audio"),
-    ],
-    outputs=gr.Textbox(label="Transcription", rtl=True, elem_classes="textbox1"),
-    title="Transcribe Dhivehi Audio",
-    allow_flagging="never",
+    inputs=gr.Audio(sources=["microphone","upload"], type="filepath"),
+    outputs="text",
+    title="Whisper Small Demo",
+    description="Realtime demo for English speech recognition, powered by the open-source Whisper model.",
 )
-
-with demo:
-    gr.TabbedInterface([tab_audio], ["Audio"])
-
-
-def install_requirements():
-    requirements_path = 'requirements.txt'
-    
-    # Check if requirements.txt exists
-    if not os.path.exists(requirements_path):
-        print("Error: requirements.txt not found")
-        return False
-        
-    try:
-        print("Installing requirements...")
-        # Using --no-cache-dir to avoid memory issues
-        subprocess.check_call([
-            sys.executable, 
-            "-m", 
-            "pip", 
-            "install", 
-            "-r", 
-            requirements_path,
-            "--no-cache-dir"
-        ])
-        print("Successfully installed all requirements")
-        return True
-    except subprocess.CalledProcessError as e:
-        print(f"Error installing requirements: {e}")
-        return False
-    except Exception as e:
-        print(f"Unexpected error: {e}")
-        return False
-
-# Launch the interface
-if name == "main":
-    success = install_requirements()
-    if success:
-        print("All requirements installed successfully")
-      
-        from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
-        import torch
-        import torchaudio
-        import numpy as np
-
-        # Device and dtype configuration
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-        
-        MODEL_NAME = "Devion333/wav2vec2-xls-r-300m-dv" # Trained on common voice with ngram from news corpa 
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/wav2vec2-large-mms-1b-cv" # Trained on Common Voice Data (Unknown Hours)
-        # MODEL_NAME =  "/home/rusputin/lab/audio/fine-tunes/whisper-small-dv-syn-md" # Trained on 100% Synthetic Data (150 Hours)   
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-small-cv" # Trained on Common Voice Data (Unknown Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-medium-dv-syn-md" # Trained on 100% Synthetic Data (150 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-medium-cv" # Trained on Common Voice Data (Unknown Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-large-v3-dv-syn-md" # Trained on 100% Synthetic Data (150 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-large-v3-cv" # Trained on Common Voice Data (Unknown Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/whisper-large-v3-calls-md" # Trained on phone calls (65 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/wav2vec2-large-mms-1b-calls-md" # Trained on phone calls (65 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/wav2vec2-large-xlsr-calls-md" # Trained on phone calls (23 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/wav2vec2-large-xlsr-dv-syn-md" # Trained on 100% Synthetic Data (80 Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/dhivehi-asr-full-ctc" # Trained on multiple datasets (350+ Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/dhivehi-asr-full-ctc-v2" # Trained on multiple datasets (350+ Hours)
-        # MODEL_NAME = "/home/rusputin/lab/audio/fine-tunes/dhivehi-asr-full-whisper-v3" # Trained on multiple datasets (350+ Hours)
-
-        # Load model and processor with LM
-        processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_NAME)
-        model = Wav2Vec2ForCTC.from_pretrained(
-            MODEL_NAME,
-            torch_dtype=torch_dtype
-        ).to(device)
-
-        MAX_LENGTH = 120 # 2 minutes
-        MIN_LENGTH = 1 # 1 second
-
-        demo.launch()
-    else:
-        print("Failed to install some requirements")
+demo.launch()