Update app.py

app.py

@@ -4,76 +4,101 @@ from transformers import pipeline
 import torch
 import ffmpeg  # Make sure it's ffmpeg-python
 
-# Check if GPU is available
-use_gpu = torch.cuda.is_available()
-
- 
-# Configure the pipeline to use the GPU if available
-if use_gpu:
-    p = pipeline("automatic-speech-recognition",
-                 model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h", device=0)
-else:
-    p = pipeline("automatic-speech-recognition",
-                 model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h")
-
-def extract_audio_from_m3u8(url):
-    try:
-        output_file = "output_audio.aac"
-        ffmpeg.input(url).output(output_file).run(overwrite_output=True)
-        return output_file
-    except Exception as e:
-        return f"An error occurred: {e}"
-
-def transcribe_function(audio, state, uploaded_audio, m3u8_url):
-    if m3u8_url:
-        audio = extract_audio_from_m3u8(m3u8_url)
-
-    if uploaded_audio is not None:
-        audio = uploaded_audio
-
-    if not audio:
-        return {state_var: state, transcription_var: state}  # Return a meaningful message
-
-    try:
-        time.sleep(3)
-        text = p(audio, chunk_length_s= 50)["text"]
-        state += text + "\n"
-        return {state_var: state, transcription_var: state}
-    except Exception as e:
-        return {transcription_var: "An error occurred during transcription.", state_var: state}  # Handle other exceptions
-
-# ... [most of your code remains unchanged]
-
-def reset_output(transcription, state):
-    """Function to reset the state to an empty string."""
-    return "", ""
-
-with gr.Blocks() as demo:
-    state_var = gr.State("")
-
-    with gr.Row():
-        with gr.Column():
-            microphone = gr.Audio(source="microphone", type="filepath", label="Microphone")
-            uploaded_audio = gr.Audio(label="Upload Audio File", type="filepath", source="upload")
-            m3u8_url = gr.Textbox(label="m3u8 URL | E.g.: from kvf.fo or logting.fo")
-
-        with gr.Column():
-            transcription_var = gr.Textbox(type="text", label="Transcription", readonly=True)
-
-    with gr.Row():
-        transcribe_button = gr.Button("Transcribe")
-        reset_button = gr.Button("Reset output")
-
-    transcribe_button.click(
-        transcribe_function,
-        [microphone, state_var, uploaded_audio, m3u8_url],
-        [transcription_var, state_var]
-    )
-
-    reset_button.click(
-        reset_output,
-        [transcription_var, state_var],
-        [transcription_var, state_var]
-    )
-
-demo.launch()
+def main():
+    # Check if GPU is available
+    use_gpu = torch.cuda.is_available()
+
+    # Configure the pipeline to use the GPU if available
+    if use_gpu:
+        p = pipeline(
+            "automatic-speech-recognition",
+            model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h",
+            device=0
+        )
+    else:
+        p = pipeline(
+            "automatic-speech-recognition",
+            model="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-faroese-100h"
+        )
+
+    def extract_audio_from_m3u8(url):
+        try:
+            output_file = "output_audio.aac"
+            ffmpeg.input(url).output(output_file).run(overwrite_output=True)
+            return output_file
+        except Exception as e:
+            return f"An error occurred: {e}"
+
+    def transcribe_function(audio, state, uploaded_audio, m3u8_url):
+        if m3u8_url:
+            audio = extract_audio_from_m3u8(m3u8_url)
+
+        if uploaded_audio is not None:
+            audio = uploaded_audio
+
+        if not audio:
+            # Return a meaningful message; no audio found
+            return {state_var: state, transcription_var: state}
+
+        try:
+            time.sleep(3)
+            text = p(audio, chunk_length_s=50)["text"]
+            state += text + "\n"
+            return {state_var: state, transcription_var: state}
+        except Exception as e:
+            return {
+                transcription_var: "An error occurred during transcription.",
+                state_var: state
+            }
+
+    def reset_output(transcription, state):
+        """Function to reset the state to an empty string."""
+        return "", ""
+
+    with gr.Blocks() as demo:
+        state_var = gr.State("")
+
+        with gr.Row():
+            with gr.Column():
+                microphone = gr.Audio(
+                    source="microphone",
+                    type="filepath",
+                    label="Microphone"
+                )
+                uploaded_audio = gr.Audio(
+                    label="Upload Audio File",
+                    type="filepath",
+                    source="upload"
+                )
+                m3u8_url = gr.Textbox(
+                    label="m3u8 URL | E.g.: from kvf.fo or logting.fo"
+                )
+
+            with gr.Column():
+                transcription_var = gr.Textbox(
+                    type="text",
+                    label="Transcription",
+                    readonly=True
+                )
+
+        with gr.Row():
+            transcribe_button = gr.Button("Transcribe")
+            reset_button = gr.Button("Reset output")
+
+        transcribe_button.click(
+            transcribe_function,
+            [microphone, state_var, uploaded_audio, m3u8_url],
+            [transcription_var, state_var]
+        )
+
+        reset_button.click(
+            reset_output,
+            [transcription_var, state_var],
+            [transcription_var, state_var]
+        )
+
+    # Launch with the latest Gradio features
+    demo.launch()
+
+if __name__ == "__main__":
+    main()