Create app.py

app.py

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+Alexa-NLU-Clone
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+app.py
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+51be472
+over 2 years ago
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+4.87 kB
+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
+
+SAMPLE_RATE = 16_000
+
+models = {}
+
+models_paths = {
+    "en-US": "jonatasgrosman/wav2vec2-large-xlsr-53-english",
+    "fr-FR": "jonatasgrosman/wav2vec2-large-xlsr-53-french",
+    "nl-NL": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch",
+    "pl-PL": "jonatasgrosman/wav2vec2-large-xlsr-53-polish",
+    "it-IT": "jonatasgrosman/wav2vec2-large-xlsr-53-italian",
+    "ru-RU": "jonatasgrosman/wav2vec2-large-xlsr-53-russian",
+    "pt-PT": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese",
+    "de-DE": "jonatasgrosman/wav2vec2-large-xlsr-53-german",
+    "es-ES": "jonatasgrosman/wav2vec2-large-xlsr-53-spanish",
+    "ja-JP": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
+    "ar-SA": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic",
+    "fi-FI": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish",
+    "hu-HU": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian",
+    "zh-CN": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn",
+    "el-GR": "jonatasgrosman/wav2vec2-large-xlsr-53-greek",
+}
+
+# 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 = './wavs/'
+examples = sorted(glob(os.path.join(EXAMPLE_DIR, '*.wav')))
+examples = [[e, e.split("=")[0].split("/")[-1]] for e in examples]
+
+def transcribe(audio_path, lang_code):
+        
+    speech_array, sampling_rate = librosa.load(audio_path, sr=16_000)
+
+    if lang_code not in models:
+        models[lang_code] = {}
+        models[lang_code]["processor"] = Wav2Vec2Processor.from_pretrained(models_paths[lang_code])
+        models[lang_code]["model"] = Wav2Vec2ForCTC.from_pretrained(models_paths[lang_code])
+    
+    # Load model
+    processor_asr = models[lang_code]["processor"]
+    model_asr = models[lang_code]["model"]
+
+    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 predict(wav_file, lang_code):
+
+    if lang_code not in models_paths.keys():
+
+        return {
+            "The language code is unknown!"
+        }
+    
+    text = transcribe(wav_file, lang_code).replace("apizza","a pizza") + " ."
+
+    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,
+    }
+
+iface = gr.Interface(
+    predict,
+    title='Sentiment Analysis project',
+    description='Upload your wav file to test the models (<i>First execution take about 20s to 30s, then next run in less than 1s</i>)',
+    # thumbnail="",
+    inputs=[
+        gr.inputs.Audio(label='wav file', source='microphone', type='filepath'),
+        gr.inputs.Dropdown(choices=list(models_paths.keys())),
+    ],
+    outputs=[
+        gr.outputs.JSON(label='ASR -> Slot Recognition + Intent Classification + Language Classification'),
+    ],
+    examples=examples,
+    article='Sentiment Analysis project',
+)
+
+iface.launch()