Add checkpoints
Browse files- README.md +69 -0
- all_results.json +23 -0
- config.json +84 -0
- config_train.json +162 -0
- eval_results.json +12 -0
- preprocessor_config.json +9 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- train_results.json +14 -0
- trainer_state.json +1189 -0
- training_args.bin +3 -0
- vocab.json +1 -0
README.md
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---
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language: pt
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datasets:
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- Common Voice
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metrics:
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- wer
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tags:
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- audio
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- speech
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- wav2vec2
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- pt
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- portuguese-speech-corpus
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- automatic-speech-recognition
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- speech
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- PyTorch
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license: apache-2.0
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model-index:
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- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Portuguese
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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metrics:
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- name: Test Common Voice 7.0 WER
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type: wer
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value: 63.90
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---
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# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
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[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset (TTS-Portuguese Corpus).
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# Use this model
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```python
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from transformers import AutoTokenizer, Wav2Vec2ForCTC
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tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese")
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model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese")
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```
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# Results
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For the results check the [article (Soon)]()
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# Example test with Common Voice Dataset
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```python
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dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
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resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
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def map_to_array(batch):
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speech, _ = torchaudio.load(batch["path"])
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batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
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batch["sampling_rate"] = resampler.new_freq
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
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return batch
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```
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```python
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ds = dataset.map(map_to_array)
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result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
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print(wer.compute(predictions=result["predicted"], references=result["target"]))
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```
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all_results.json
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{
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"epoch": 129.99,
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"eval_loss": 0.6900125741958618,
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"eval_mem_cpu_alloc_delta": 152395776,
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"eval_mem_cpu_peaked_delta": 122880,
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"eval_mem_gpu_alloc_delta": 0,
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"eval_mem_gpu_peaked_delta": 7244027392,
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"eval_runtime": 49.6987,
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"eval_samples": 500,
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"eval_samples_per_second": 10.061,
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"eval_wer": 0.4532554257095159,
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"init_mem_cpu_alloc_delta": 3913703424,
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"init_mem_cpu_peaked_delta": 1208139776,
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"init_mem_gpu_alloc_delta": 1261939712,
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"init_mem_gpu_peaked_delta": 0,
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"train_mem_cpu_alloc_delta": 2117373952,
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"train_mem_cpu_peaked_delta": 24576,
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"train_mem_gpu_alloc_delta": 3778624512,
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"train_mem_gpu_peaked_delta": 9083709440,
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"train_runtime": 94419.6153,
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"train_samples": 3083,
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"train_samples_per_second": 0.024
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}
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config.json
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{
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"_name_or_path": "facebook/wav2vec2-large-100k-voxpopuli",
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"activation_dropout": 0.0,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"codevector_dim": 768,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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2
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],
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"conv_stride": [
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5,
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.1,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.0,
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"mask_channel_length": 10,
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"mask_channel_min_space": 1,
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"mask_channel_other": 0.0,
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"mask_channel_prob": 0.0,
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"mask_channel_selection": "static",
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"mask_feature_length": 10,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_space": 1,
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"mask_time_other": 0.0,
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"mask_time_prob": 0.05,
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"mask_time_selection": "static",
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"model_type": "wav2vec2",
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"pad_token_id": 0,
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"proj_codevector_dim": 768,
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"transformers_version": "4.6.1",
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"vocab_size": 45
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}
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config_train.json
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{
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"run_name": "Wav2Vec-fine-tuning-TEDx",
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"run_description": "Fine tuning TEDx",
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"seed": 42,
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// AUDIO PARAMS
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"sampling_rate": 16000,
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// VOCABULARY PARAMETERS
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"vocab":{
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"vocab_path": "example/vocab_example.json", // generic vocab for Portuguese
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"blank": "<pad>", // blank token for padding
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"silence": "|", // token between words
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"unk": "<unk>" // unk token
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},
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// TRAINING
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"batch_size": 8, // Batch size for training.
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"mixed_precision": true, // level of optimization with NVIDIA's apex feature for automatic mixed FP16/FP32 precision (AMP), NOTE: currently only O1 is supported, and use "O1" to activate.
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"early_stop_epochs": 10, // If 0 disabled else Number of epochs for stop training with validation loss dont decrease
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"preprocess_dataset": false, // if true, the dataset will be pre-processed and saved in disk, otherwise the audio files will be loaded in each step. Preprocessing makes training faster, but requires much more disk space.
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// OPTIMIZER
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"epochs": 140, // total number of epochs to train.
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"lr": 0.00003, // Initial learning rate.
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"gradient_accumulation_steps": 24,
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// LOGGING
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"logging_steps": 100, // Number of steps to plot.
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"load_best_model_at_end": true,
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"save_total_limit": 3,
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"warmup_ratio": 0.06666666667, // 0 disable Ratio of total training steps used for a linear warmup from 0 to learning_rate
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"warmup_steps": 0, // 0 disable Number of steps used for a linear warmup from 0 to learning_rate
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// DATA LOADING
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are goo
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// MODEL
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"freeze_feature_extractor": true, // Whether to freeze the feature extractor layers of the model.
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"attention_dropout": 0.1, // The dropout ratio for the attention probabilities.
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"activation_dropout": 0.1, // The dropout ratio for activations inside the fully connected layer.
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"hidden_dropout": 0.1, // The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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"feat_proj_dropout": 0.1, // The dropout probabilitiy for all 1D convolutional layers in feature extractor.
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"mask_time_prob": 0.05, // Propability of each feature vector along the time axis to be chosen as the start of the vector span to be masked.
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"layerdrop": 0.0, // The LayerDrop probability.
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"gradient_checkpointing": true, // If True, use gradient checkpointing to save memory at the expense of slower backward pass.
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// ToDo: Implement Time mask and Frequency Mask
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"audio_augmentation":[
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// additive noise and room impulse response (RIR) simulation similar to: https://arxiv.org/pdf/2009.14153.pdf
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{
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"name": "additive",
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"sounds_path":"/raid/datasets/DA/musan/speech/", // download: https://www.openslr.org/17/
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"lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory
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"min_snr_in_db": 13.0,
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"max_snr_in_db": 20.0,
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// "sample_rate": 16000,
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"p": 0.25
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},
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{
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"name": "additive",
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"sounds_path":"/raid/datasets/DA/musan/music/", // download: https://www.openslr.org/17/
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"lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory
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"min_snr_in_db": 5.0,
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"max_snr_in_db": 15.0,
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// "sample_rate": 16000,
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"p": 0.25
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},
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{
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"name": "additive",
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"sounds_path":"/raid/datasets/DA/musan/noise/", // download: https://www.openslr.org/17/
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+
"lru_cache_size": 32, // Maximum size of the LRU cache for storing noise files in memory
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"min_snr_in_db": 0.0,
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"max_snr_in_db": 15.0,
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// "sample_rate": 16000,
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"p": 0.25
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},
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// rir filter proposed by: https://ieeexplore.ieee.org/document/7953152
|
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{
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"name": "rir",
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"ir_path": "/raid/datasets/DA/RIRS_NOISES/simulated_rirs/", // download: https://www.openslr.org/28/
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"lru_cache_size": 128, // Maximum size of the LRU cache for storing noise files in memory
|
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// "sample_rate": 16000,
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"p": 0.25
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}
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,
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// {
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// "name": "gain",
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// "min_gain_in_db": -18.0,
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// "max_gain_in_db": 6,
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// "p": 0.25 // propability of apply this method, 0 is disable
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+
// },
|
92 |
+
{
|
93 |
+
"name": "pitch_shift",
|
94 |
+
"min_semitones": -4,
|
95 |
+
"max_semitones": 4,
|
96 |
+
"p": 0.25 // propability of apply this method, 0 is disable
|
97 |
+
},
|
98 |
+
{
|
99 |
+
"name": "gaussian",
|
100 |
+
"min_amplitude": 0.0001,
|
101 |
+
"max_amplitude": 0.001,
|
102 |
+
"p": 0.25 // propability of apply this method, 0 is disable
|
103 |
+
}
|
104 |
+
],
|
105 |
+
// PATHS
|
106 |
+
"output_path": "../checkpoints/YourTTS2ASR/Wav2Vec-voxpopuli/one-speaker/just-TTS/PT/140-epoch-high-bs/",
|
107 |
+
// CACHE
|
108 |
+
"dataset_cache": "../datasets/",
|
109 |
+
|
110 |
+
// DATASETS
|
111 |
+
"datasets":{
|
112 |
+
"files_path": "/raid/datasets/TTS-Portuguese-Corpus/", // relative path for audios It's will be join with the CS
|
113 |
+
"train":
|
114 |
+
[
|
115 |
+
// this dicts is pass directly for the load dataset see the documentation: https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset
|
116 |
+
{
|
117 |
+
"name": "csv",
|
118 |
+
"path": "csv",
|
119 |
+
|
120 |
+
"data_files": ["/raid/datasets/TTS-Portuguese-Corpus/train_TTS-Portuguese_Corpus_metadata_converted_to_ASR.csv"], // csv files
|
121 |
+
"text_column": "text",
|
122 |
+
"path_column": "file_path"
|
123 |
+
}
|
124 |
+
]
|
125 |
+
,
|
126 |
+
"devel":
|
127 |
+
[
|
128 |
+
{
|
129 |
+
"name": "csv",
|
130 |
+
"path": "csv",
|
131 |
+
"data_files": ["/raid/datasets/TTS-Portuguese-Corpus/eval_TTS-Portuguese_Corpus_metadata_converted_to_ASR.csv"], // csv files
|
132 |
+
"text_column": "text",
|
133 |
+
"path_column": "file_path"
|
134 |
+
}
|
135 |
+
]
|
136 |
+
,
|
137 |
+
"test":
|
138 |
+
{
|
139 |
+
"name": "csv",
|
140 |
+
"path": "csv",
|
141 |
+
"data_files": ["/raid/datasets/Common_Voice/cv-corpus-7.0-2021-07-21/pt/test_converted.csv"], // csv files
|
142 |
+
"text_column": "text",
|
143 |
+
"path_column": "file_path"
|
144 |
+
}
|
145 |
+
|
146 |
+
}//,
|
147 |
+
// used only for test
|
148 |
+
// "KenLM":{
|
149 |
+
// "kenlm_model_path": "../../kenLM/binaries/subtitle/4-gram/lm.binary", // Path for KenLM model
|
150 |
+
// "lexicon_path": "example/lexicon.lst", // file with all words for limit the decoder search
|
151 |
+
// "beam": 2048,
|
152 |
+
// "nbest": 1,
|
153 |
+
// "beam_threshold": 25,
|
154 |
+
// "lm_weight": 1,
|
155 |
+
// "word_score": -1,
|
156 |
+
// "sil_weight": 0
|
157 |
+
// }
|
158 |
+
|
159 |
+
|
160 |
+
|
161 |
+
}
|
162 |
+
|
eval_results.json
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 129.99,
|
3 |
+
"eval_loss": 0.6900125741958618,
|
4 |
+
"eval_mem_cpu_alloc_delta": 152395776,
|
5 |
+
"eval_mem_cpu_peaked_delta": 122880,
|
6 |
+
"eval_mem_gpu_alloc_delta": 0,
|
7 |
+
"eval_mem_gpu_peaked_delta": 7244027392,
|
8 |
+
"eval_runtime": 49.6987,
|
9 |
+
"eval_samples": 500,
|
10 |
+
"eval_samples_per_second": 10.061,
|
11 |
+
"eval_wer": 0.4532554257095159
|
12 |
+
}
|
preprocessor_config.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"do_normalize": true,
|
3 |
+
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
4 |
+
"feature_size": 1,
|
5 |
+
"padding_side": "right",
|
6 |
+
"padding_value": 0.0,
|
7 |
+
"return_attention_mask": true,
|
8 |
+
"sampling_rate": 16000
|
9 |
+
}
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6a0b7787c4b812154e4d737af312cac8d7b1f3197d72e84feb6999aa3007be90
|
3 |
+
size 1262108145
|
special_tokens_map.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": false, "word_delimiter_token": "|"}
|
train_results.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 129.99,
|
3 |
+
"init_mem_cpu_alloc_delta": 3913703424,
|
4 |
+
"init_mem_cpu_peaked_delta": 1208139776,
|
5 |
+
"init_mem_gpu_alloc_delta": 1261939712,
|
6 |
+
"init_mem_gpu_peaked_delta": 0,
|
7 |
+
"train_mem_cpu_alloc_delta": 2117373952,
|
8 |
+
"train_mem_cpu_peaked_delta": 24576,
|
9 |
+
"train_mem_gpu_alloc_delta": 3778624512,
|
10 |
+
"train_mem_gpu_peaked_delta": 9083709440,
|
11 |
+
"train_runtime": 94419.6153,
|
12 |
+
"train_samples": 3083,
|
13 |
+
"train_samples_per_second": 0.024
|
14 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,1189 @@
|
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|
|
|
|
|
|
|
|
|
|
|
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training_args.bin
ADDED
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version https://git-lfs.github.com/spec/v1
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vocab.json
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