Added ASR model files

README.md

@@ -10,3 +10,17 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# ASR Transcription Service
+
+This is an Automatic Speech Recognition (ASR) system deployed on Hugging Face Spaces using Gradio.
+
+## Features
+- Supports **Tunisian Arabic (tn)**, **French (fr)**, and **English (en)**
+- Voice Activity Detection (VAD) for noise removal
+- Model based on OpenAI Whisper and a fine-tuned Tunisian ASR model
+
+## How to Use
+1. Upload an audio file or record using the microphone.
+2. Select the transcription language.
+3. Get the transcribed text!

app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import torch
+import librosa
+import numpy as np
+import webrtcvad
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, pipeline
+
+# Model names
+TN_MODEL_NAME = "amenIKh/Tunisian_Checkpoint12"
+WHISPER_MODEL_NAME = "openai/whisper-small"
+
+# Initialize pipelines
+pipe_tn = pipeline(
+    task="automatic-speech-recognition",
+    model=TN_MODEL_NAME,
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+# Load Whisper model and processor
+whisper_model = WhisperForConditionalGeneration.from_pretrained(WHISPER_MODEL_NAME)
+whisper_processor = WhisperProcessor.from_pretrained(WHISPER_MODEL_NAME)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+whisper_model.to(device)
+
+# Function to apply VAD
+def apply_vad(audio, sr, frame_duration_ms=30):
+    vad = webrtcvad.Vad()
+    vad.set_mode(3)  # Aggressiveness mode, higher value is more aggressive
+
+    frame_size = int(sr * frame_duration_ms / 1000)
+    offset = 0
+    voiced_frames = []
+
+    while offset + frame_size < len(audio):
+        frame = audio[offset:offset + frame_size].astype(np.int16)
+        is_speech = vad.is_speech(frame.tobytes(), sr)
+
+        if is_speech:
+            voiced_frames.append(frame)
+
+        offset += frame_size
+
+    if len(voiced_frames) == 0:
+        return audio  # Return original audio if no voiced frames are detected
+
+    voiced_audio = np.concatenate(voiced_frames)
+    return voiced_audio
+
+# Function to transcribe audio based on language
+def transcribe_audio(audio, language):
+    try:
+        # Load audio
+        sr = 16000  # Assuming the audio is in 16kHz; adjust if necessary
+        audio, _ = librosa.load(audio, sr=sr)
+
+        # Apply VAD
+        voiced_audio = apply_vad(audio, sr)
+
+        # Select the correct model based on language
+        if language == "tn":
+            result = pipe_tn(voiced_audio)
+            transcription = result.get("text", "")
+        elif language in ["fr", "en"]:
+            forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language=language, task="transcribe")
+            input_features = whisper_processor(voiced_audio, return_tensors="pt").input_features.to(device)
+            generated_ids = whisper_model.generate(
+                input_features, 
+                forced_decoder_ids=forced_decoder_ids
+            )
+            transcription = whisper_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        else:
+            return "Unsupported language specified"
+
+        return transcription
+    except Exception as e:
+        return f"An unexpected error occurred: {str(e)}"
+# Define Gradio interface
+def gradio_interface(audio, language):
+    try:
+        # Extract the file path or microphone input from the Gradio audio input
+        if isinstance(audio, tuple):
+            temp_file_path = audio[0]  # For microphone recordings, extract file path from the tuple
+        else:
+            temp_file_path = audio  # For uploaded files
+
+        # Perform transcription
+        result = transcribe_audio(temp_file_path, language)
+        
+        return result
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Create the Gradio app
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Audio(sources=["upload","microphone"],type="filepath", label="Upload Audio"),
+        gr.Dropdown(choices=["tn", "fr", "en"], label="Select Language")
+    ],
+    outputs="text",
+    title="ASR Transcription Service",
+    description="Upload an audio file and select the language to transcribe the audio."
+)
+
+# Add the custom HTML with background image
+iface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+gradio
+transformers
+librosa
+webrtcvad