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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) | |
''' |