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| #Build a shareable app with Gradio | |
| import torch | |
| from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline | |
| #from datasets import load_dataset | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
| from transformers import pipeline | |
| asr = pipeline(task="automatic-speech-recognition", | |
| model="distil-whisper/distil-small.en") | |
| import os | |
| import gradio as gr | |
| demo = gr.Blocks() | |
| def transcribe_speech(filepath): | |
| if filepath is None: | |
| gr.Warning("No audio found, please retry.") | |
| return "" | |
| output = asr(filepath) | |
| return output["text"] | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(sources="microphone", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=3), | |
| allow_flagging="never") | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(sources="upload", | |
| type="filepath"), | |
| outputs=gr.Textbox(label="Transcription", | |
| lines=3), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface( | |
| [mic_transcribe, | |
| file_transcribe], | |
| ["Transcribe Microphone", | |
| "Transcribe Audio File"], | |
| ) | |
| #demo.launch(server_port=int(os.environ['PORT1'])) | |
| demo.launch(server_port=int(os.environ.get('PORT1',8080))) | |
| ''' | |
| import soundfile as sf | |
| import io | |
| audio, sampling_rate = sf.read('output.wav') | |
| print(audio.shape) | |
| ''' |