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# 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
# 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=False)
# def transcribe_function(new_chunk, state):
# try:
# sr, y = new_chunk
# 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
# 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=False)
# def transcribe_function(new_chunk, state):
# try:
# sr, y = new_chunk
# 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
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=False)
def ensure_mono(y):
if len(y.shape) > 1 and y.shape[1] > 1:
y = np.mean(y, axis=1)
return y
def transcribe_function(new_chunk, state):
try:
sr, y = new_chunk
except TypeError:
print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
return state, "", None
y = ensure_mono(y)
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
def upload_transcribe(file):
sr, y = file
y = ensure_mono(y)
y = y.astype(np.float32) / np.max(np.abs(y))
result = pipe_asr({"array": y, "sampling_rate": sr}, return_timestamps=False)
return result.get("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")
audio_upload = gr.Audio(sources="upload", type='numpy', label="Upload Audio File")
with gr.Column():
output_text = gr.Textbox(label="Transcription")
upload_text = gr.Textbox(label="Uploaded Audio Transcription")
audio_input.stream(transcribe_function, inputs=[audio_input, state], outputs=[state, output_text], api_name="SAMLOne_real_time")
audio_upload.change(upload_transcribe, inputs=audio_upload, outputs=upload_text)
demo.launch(show_error=True)
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