File size: 2,764 Bytes
7054162 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import gradio as gr
import torch
import whisper
import warnings
warnings.filterwarnings('ignore')
from transformers import pipeline
import os
MODEL_NAME = "openai/whisper-small"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device)
emotion_classifier = pipeline("text-classification",model='MilaNLProc/xlm-emo-t', return_all_scores=True)
def transcribe(microphone, file_upload, task):
output = ""
if (microphone is not None) and (file_upload is not None):
warn_output = (
"WARNING: You've uploaded an audio file and used the microphone. "
"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
)
elif (microphone is None) and (file_upload is None):
raise gr.Error("You have to either use the microphone or upload an audio file")
file = microphone if microphone is not None else file_upload
text = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["text"]
return output + text
def translate_and_classify(audio):
text_result = transcribe(audio, None, "transcribe")
emotion = emotion_classifier(text_result)
detected_emotion = {}
for emotion in emotion[0]:
detected_emotion[emotion["label"]] = emotion["score"]
return text_result, detected_emotion
with gr.Blocks() as demo:
gr.Markdown(
""" # Emotion Detection from Speech
##### Detection of anger, sadness, joy, fear in speech using OpenAI Whisper and XLM-RoBERTa
""")
with gr.Column():
with gr.Tab("Record Audio"):
# The 'source' argument is no longer supported, use 'sources' instead
audio_input_r = gr.Audio(label = 'Record Audio Input',sources=["microphone"],type="filepath")
transcribe_audio_r = gr.Button('Transcribe')
with gr.Tab("Upload Audio as File"):
# The 'source' argument is no longer supported, use 'sources' instead
audio_input_u = gr.Audio(label = 'Upload Audio',sources=["upload"],type="filepath")
transcribe_audio_u = gr.Button('Transcribe')
with gr.Row():
transcript_output = gr.Textbox(label="Transcription in the language of speech/audio", lines = 3)
emotion_output = gr.Label(label = "Detected Emotion")
transcribe_audio_r.click(translate_and_classify, inputs = audio_input_r, outputs = [transcript_output,emotion_output])
transcribe_audio_u.click(translate_and_classify, inputs = audio_input_u, outputs = [transcript_output,emotion_output])
demo.launch(share=True) |