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from transformers import pipeline | |
import gradio as gr | |
from pyctcdecode import BeamSearchDecoderCTC | |
from Aniemore import EmotionFromVoice | |
import os | |
# emo = pipeline("audio-classification", model="Aniemore/wav2vec2-xlsr-53-russian-emotion-recognition", trust_remote_code=True) | |
emo = EmotionFromVoice() | |
def transcribe(audio): | |
print(audio) | |
def get_asr_interface(): | |
return gr.Interface( | |
fn=transcribe, | |
inputs=[ | |
gr.inputs.Audio(source="microphone", type="filepath") | |
], | |
outputs=[ | |
"textbox" | |
]) | |
interfaces = [ | |
get_asr_interface() | |
] | |
names = [ | |
"ASR" | |
] | |
gr.TabbedInterface(interfaces, names).launch(server_name = "0.0.0.0", enable_queue=False) |