Update app.py

app.py

@@ -1,52 +1,3 @@
-# import gradio as gr
-# import numpy as np
-# import torch
-# from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
-
-# model_id = 'openai/whisper-large-v3'
-# device = "cuda:0" if torch.cuda.is_available() else "cpu"
-# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-# model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
-# processor = AutoProcessor.from_pretrained(model_id)
-
-# pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, return_timestamps=True)
-
-# def transcribe_function(new_chunk, state):
-#     try:
-#         sr, y = new_chunk[0], new_chunk[1]
-#     except TypeError:
-#         print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
-#         return state, "", None
-
-#     y = y.astype(np.float32) / np.max(np.abs(y))
-
-#     if state is not None:
-#         state = np.concatenate([state, y])
-#     else:
-#         state = y
-
-#     result = pipe_asr({"array": state, "sampling_rate": sr}, return_timestamps=False)
-
-#     full_text = result.get("text", "")
-    
-#     return state, full_text
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("# Voice to Text Transcription")
-    
-#     state = gr.State(None)
-
-#     with gr.Row():
-#         with gr.Column():
-#             audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', label="Microphone Input")
-#         with gr.Column():
-#             output_text = gr.Textbox(label="Transcription")
-
-#     audio_input.stream(transcribe_function, inputs=[audio_input, state], outputs=[state, output_text], api_name="SAMLOne_real_time")
-
-# demo.launch(show_error=True)
-
-
 import gradio as gr
 import numpy as np
 import torch
@@ -62,8 +13,7 @@ pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=proce
 
 def transcribe_function(new_chunk, state):
     try:
-        sr = new_chunk["sampling_rate"]
-        y = np.array(new_chunk["array"])
+        sr, y = new_chunk[0], new_chunk[1]
     except TypeError:
         print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
         return state, "", None
@@ -96,3 +46,5 @@ with gr.Blocks() as demo:
 
 demo.launch(show_error=True)
 
+
+