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import gradio as gr
import os
import torch
import librosa
from glob import glob
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline, AutoModelForTokenClassification, TokenClassificationPipeline, Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
# ASR
model_name = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
processor_asr = Wav2Vec2Processor.from_pretrained(model_name)
model_asr = Wav2Vec2ForCTC.from_pretrained(model_name)
# Classifier Intent
model_name = 'qanastek/XLMRoberta-Alexa-Intents-Classification'
tokenizer_intent = AutoTokenizer.from_pretrained(model_name)
model_intent = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier_intent = TextClassificationPipeline(model=model_intent, tokenizer=tokenizer_intent)
# Classifier Language
model_name = 'qanastek/51-languages-classifier'
tokenizer_langs = AutoTokenizer.from_pretrained(model_name)
model_langs = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier_language = TextClassificationPipeline(model=model_langs, tokenizer=tokenizer_langs)
# NER Extractor
model_name = 'qanastek/XLMRoberta-Alexa-Intents-NER-NLU'
tokenizer_ner = AutoTokenizer.from_pretrained(model_name)
model_ner = AutoModelForTokenClassification.from_pretrained(model_name)
predict_ner = TokenClassificationPipeline(model=model_ner, tokenizer=tokenizer_ner)
EXAMPLE_DIR = './'
examples = sorted(glob(os.path.join(EXAMPLE_DIR, '*.wav')))
def transcribe(audio_path):
speech_array, sampling_rate = librosa.load(audio_path, sr=16_000)
inputs = processor_asr(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model_asr(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
return processor_asr.batch_decode(predicted_ids)[0]
def getUniform(text):
idx = 0
res = {}
for t in text:
raw = t["entity"].replace("B-","").replace("I-","")
word = t["word"].replace("▁","")
if "B-" in t["entity"]:
res[f"{raw}|{idx}"] = [word]
idx += 1
else:
res[f"{raw}|{idx}"].append(word)
res = [(r.split("|")[0], res[r]) for r in res]
return res
def process(path):
text = transcribe(path)
intent_class = classifier_intent(text)[0]["label"]
language_class = classifier_language(text)[0]["label"]
named_entities = getUniform(predict_ner(text))
return {
"text": text,
"language": language_class,
"intent_class": intent_class,
"named_entities": named_entities,
}
audio_paths = [
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/set-the-volume-to-low.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/tell-me-a-joke.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/tell me the artist of this song.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/order-a-pizza.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/TTS_1/tell-me-a-good-joke.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/TTS_1/order me a pizza.wav",
"/users/ylabrak/Alexa_NLU/Pipeline/wavs/TTS_1/tell-me-the-artist-of-this-song.wav",
]
def predict(wav_file):
res = process(wav_file)
return res
# iface = gr.Interface(fn=predict, inputs="text", outputs="text")
iface = gr.Interface(
predict,
title='Alexa NLU Clone',
description='Upload your wav file to test the model',
inputs=[
gr.inputs.Audio(label='wav file', source='microphone', type='filepath')
],
outputs=[
gr.outputs.JSON(label='Slot Recognition + Intent Classification + Language Classification + ASR'),
],
examples=examples,
article='Made with ❤️ by Yanis Labrak thanks to 🤗',
)
iface.launch() |