Update app.py

app.py

@@ -1,46 +1,46 @@
-import gradio as gr
-from transformers import pipeline
-import librosa
-import torch
-import numpy as np
-
-MODEL_NAME = 'Rezuwan/regional_asr_weights'
-#device = 0 if torch.cuda.is_available() else "cpu"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-
-transcriber = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    #device=device,
-)
-
-# Function to preprocess the audio and transcribe it
-def transcribe_audio(audio_path):
-    if audio_path is None:
-        return "No audio provided."
+# import gradio as gr
+# from transformers import pipeline
+# import librosa
+# import torch
+# import numpy as np
+
+# MODEL_NAME = 'Rezuwan/regional_asr_weights'
+# #device = 0 if torch.cuda.is_available() else "cpu"
+# BATCH_SIZE = 8
+# FILE_LIMIT_MB = 1000
+
+# transcriber = pipeline(
+#     task="automatic-speech-recognition",
+#     model=MODEL_NAME,
+#     chunk_length_s=30,
+#     #device=device,
+# )
 
-    try:
-        # If audio is a tuple, it is from the microphone (Gradio input type)
-        if isinstance(audio_path, tuple):
-            sample_rate, audio_data = audio_path  # Unpack the tuple (sample rate, numpy array)
-        else:
-            # If audio is a file, it will be a file path (for file uploads)
-            audio_data, sample_rate = librosa.load(audio_path, sr=16000)  # Load the audio file using librosa
+# # Function to preprocess the audio and transcribe it
+# def transcribe_audio(audio_path):
+#     if audio_path is None:
+#         return "No audio provided."
+
+#     try:
+#         # If audio is a tuple, it is from the microphone (Gradio input type)
+#         if isinstance(audio_path, tuple):
+#             sample_rate, audio_data = audio_path  # Unpack the tuple (sample rate, numpy array)
+#         else:
+#             # If audio is a file, it will be a file path (for file uploads)
+#             audio_data, sample_rate = librosa.load(audio_path, sr=16000)  # Load the audio file using librosa
             
             
             
-        # Convert to mono-channel if necessary (if the audio is stereo)
-        audio_data = librosa.to_mono(audio_data) if audio_data.ndim > 1 else audio_data
-        audio_data = audio_data.astype(np.float32)
-        audio_data /= np.max(np.abs(audio_data))
-        result = transcriber(audio_data)
-        return result["text"]
+#         # Convert to mono-channel if necessary (if the audio is stereo)
+#         audio_data = librosa.to_mono(audio_data) if audio_data.ndim > 1 else audio_data
+#         audio_data = audio_data.astype(np.float32)
+#         audio_data /= np.max(np.abs(audio_data))
+#         result = transcriber(audio_data)
+#         return result["text"]
 
 
-    except Exception as e:
-        return f"Error: {str(e)}"
+#     except Exception as e:
+#         return f"Error: {str(e)}"
 
 # Create the Gradio interface for both file upload and microphone input
 # iface = gr.Interface(
@@ -75,37 +75,35 @@ def transcribe_audio(audio_path):
 # )
 
 
-iface = gr.Interface(
-    fn=transcribe_audio,
-    inputs=gr.Audio(type="filepath", label="Upload or Record Audio", interactive=True),  # 'filepath' ensures file uploads provide a path for librosa to load),
-    outputs="text",
-    outputs=gr.Textbox(label="Transcription"),
-    title="Bengali Speech-to-Text with Regional Dialects",
-    description=(
-        f"""
-        Model Card: [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of arbitrary length. [Do leave a like (❤️) on the model card and this space]
-        
-        Instructions:
-        
-        1. Click on 'Record' option in the left 'Upload or Record Audio' section and record the audio. 
-        2. When done recording, click on 'Stop' button and give it some time until some waveform shows up in the 'Upload or Record Audio' section (Same goes when uploading pre-recorded audio files) and then click the 'Submit' button. 
-        3. Wait for the audio clip to be processed (This could take a while 😅. Still needs work on the inference time) and then transcription of the audio will appear on the right 'output' section.
-        4. If want to submit a trimmed version of the input, select the trimmed audio snippet and then click 'Trim' and then wait a bit until wavform 
-        shows up in the input section of the interface and then click 'Submit'.
 
+# iface.launch(share=True)
 
 
-        Note: 
-        
-        1. Since the corpus used to fine-tune this model was really small, The orthography might still not be upto the mark but it gets the work done but still needs work and manual validation.
-        
-        2.With proper data and a larger version of the corpus, I guess I'll be able to increase it's transcription performance of the Bengali speech with regional dialects.
 
-        ![](screenshot.jpg)
-        """
-    ))
+import gradio as gr
+from transformers import pipeline
+import librosa
+import numpy as np
 
+transcriber = pipeline("automatic-speech-recognition", model="Rezuwan/regional_asr_weights")
 
+def transcribe_audio(audio_path):
+    try:
+        audio_data, sample_rate = librosa.load(audio_path, sr=16000)
+        audio_data = librosa.to_mono(audio_data) if audio_data.ndim > 1 else audio_data
+        audio_data = audio_data.astype(np.float32)
+        audio_data /= np.max(np.abs(audio_data))
+        result = transcriber(audio_data)
+        return result["text"]
+    except Exception as e:
+        return f"Error: {str(e)}"
 
+iface = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.Audio(type="filepath", label="Upload or Record Audio"),
+    outputs=gr.Textbox(label="Transcription"),
+    title="🗣️ Barishal ASR Speech-to-Text",
+    description="Upload or record an audio file to transcribe Bengali speech using the Barishal ASR model."
+)
 
-iface.launch(share=True)
+iface.launch()