Update app.py

app.py

@@ -1,3 +1,51 @@
+# 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
@@ -9,11 +57,12 @@ 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)
+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=False)
 
 def transcribe_function(new_chunk, state):
     try:
-        sr, y = new_chunk[0], new_chunk[1]
+        sr = new_chunk['sampling_rate']
+        y = np.array(new_chunk['array'])
     except TypeError:
         print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
         return state, "", None
@@ -44,4 +93,4 @@ with gr.Blocks() as demo:
 
     audio_input.stream(transcribe_function, inputs=[audio_input, state], outputs=[state, output_text], api_name="SAMLOne_real_time")
 
-demo.launch(show_error=True)
+demo.launch(show_error=True)