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import gradio as gr
from transformers import pipeline
from gradio_client import Client, file
language_classifier = Client("adrien-alloreview/speechbrain-lang-id-voxlingua107-ecapa")
transcriber = Client("tensorlake/audio-extractors")
emotion_detector = pipeline(
"audio-classification",
model="HowMannyMore/wav2vec2-lg-xlsr-ur-speech-emotion-recognition",
)
model_name_rus = "IlyaGusev/rubertconv_toxic_clf"
toxic_detector = pipeline(
"text-classification",
model=model_name_rus,
tokenizer=model_name_rus,
framework="pt",
max_length=512,
truncation=True,
device=0,
)
def detect_language(file_path):
result = language_classifier.predict(param_0=file(file_path), api_name="/predict")
language_result = result["label"].split(": ")[1]
if language_result.lower() in ["russian", "belarussian", "ukrainian"]:
selected_language = "russian"
else:
selected_language = "kazakh"
return selected_language
def request_gradio(file_path, language):
try:
result = transcriber.predict(
audio_filepath=file(file_path),
task="transcribe",
batch_size=24,
chunk_length_s=30,
sampling_rate=16000,
language=language,
num_speakers=2,
min_speakers=2,
max_speakers=2,
assisted=False,
api_name="/transcribe",
)
return result
except Exception as e:
return None
def detect_emotion(audio):
res = emotion_detector(audio)
emotion_with_max_score = res[0]["label"]
return emotion_with_max_score
def detect_toxic_local(text_whisper):
res = toxic_detector([text_whisper])[0]["label"]
if res == "toxic":
return True
if res == "neutral":
return False
else:
return None
def assessment(file_path):
language = detect_language(file_path)
result_text = request_gradio(file_path, language)
result_emotion = detect_emotion(result_text)
result_toxic = detect_toxic_local(result_text)
return {"emotion": result_emotion, "toxic": result_toxic}
gradio_app = gr.Interface(
fn=assessment,
inputs=gr.Audio(sources=["upload"], type="filepath"),
outputs="json"
)
gradio_app.launch()
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