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predict_online.py

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+from transformers import (
+	Wav2Vec2FeatureExtractor,
+	Wav2Vec2CTCTokenizer,
+	Wav2Vec2Processor
+)
+import os
+import librosa
+from datasets import Dataset
+from datasets import disable_caching
+import numpy as np
+import torch.nn.functional as F
+import torch
+from model import Wav2Vec2ForCTCnCLS
+from ctctrainer import CTCTrainer
+from datacollator import DataCollatorCTCWithPadding
+
+disable_caching()
+
+cls_age_label_map = {'teens':0, 'twenties': 1, 'thirties': 2, 'fourties': 3, 'fifties': 4, 'sixties': 5, 'seventies': 6, 'eighties': 7}
+cls_age_label_class_weights = [0] * len(cls_age_label_map)
+
+cls_gender_label_map = {'female': 0, 'male': 1}
+cls_gender_label_class_weights = [0] * len(cls_gender_label_map)
+		
+model_path = "padmalcom/wav2vec2-asr-ultimate-german"
+
+tokenizer = Wav2Vec2CTCTokenizer("./vocab.json", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|")
+
+feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=False)
+
+processor = Wav2Vec2Processor(feature_extractor, tokenizer)
+
+model = Wav2Vec2ForCTCnCLS.from_pretrained(
+	model_path,
+	vocab_size=len(processor.tokenizer),
+	age_cls_len=len(cls_age_label_map),
+	gender_cls_len=len(cls_gender_label_map),
+	age_cls_weights=cls_age_label_class_weights,
+	gender_cls_weights=cls_gender_label_class_weights,
+	alpha=0.1,
+)
+
+data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True, audio_only=True)
+	
+pred_data = {'file': ['audio2.wav']}
+
+target_sr = 16000
+
+def prepare_dataset_step1(example):
+	example["speech"], example["sampling_rate"] = librosa.load(example["file"], sr=target_sr)
+	return example
+	
+def prepare_dataset_step2(batch):
+	batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values
+	return batch
+	
+val_dataset = Dataset.from_dict(pred_data)
+val_dataset = val_dataset.map(prepare_dataset_step1, load_from_cache_file=False)
+val_dataset = val_dataset.map(prepare_dataset_step2, batch_size=2, batched=True, num_proc=1, load_from_cache_file=False)
+		
+trainer = CTCTrainer(
+	model=model,
+	data_collator=data_collator,
+	eval_dataset=val_dataset,
+	tokenizer=processor.feature_extractor,
+)
+
+data_collator.audio_only=True
+predictions, labels, metrics = trainer.predict(val_dataset, metric_key_prefix="predict")
+logits_ctc, logits_age_cls, logits_gender_cls = predictions
+
+# process age classification
+pred_ids_age_cls = np.argmax(logits_age_cls, axis=-1)
+pred_age = pred_ids_age_cls[0]
+age_class = [k for k, v in cls_age_label_map.items() if v == pred_age]
+print("Predicted age: ", age_class[0])
+
+# process gender classification
+pred_ids_gender_cls = np.argmax(logits_gender_cls, axis=-1)
+pred_gender = pred_ids_gender_cls[0]
+gender_class = [k for k, v in cls_gender_label_map.items() if v == pred_gender]
+print("Predicted gender: ", gender_class[0])
+
+# process token classification
+pred_ids_ctc = np.argmax(logits_ctc, axis=-1)
+pred_str = processor.batch_decode(pred_ids_ctc, output_word_offsets=True)
+print("pred text: ", pred_str.text)