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Model: mikr/whisper-large-v3-czech-cv13 Source: Original Platform
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67
README.md
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README.md
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---
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license: apache-2.0
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base_model: openai/whisper-large-v3
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: openai/whisper-large-v3
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# openai/whisper-large-v3
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1283
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- Wer: 0.0789
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 62
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- eval_batch_size: 16
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- seed: 42
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- distributed_type: multi-GPU
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 5000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.0138 | 2.24 | 1000 | 0.0962 | 0.0863 |
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| 0.004 | 4.48 | 2000 | 0.1117 | 0.0844 |
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| 0.0015 | 6.73 | 3000 | 0.1178 | 0.0807 |
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| 0.0004 | 8.97 | 4000 | 0.1219 | 0.0792 |
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| 0.0002 | 11.21 | 5000 | 0.1283 | 0.0789 |
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### Framework versions
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- Transformers 4.36.0.dev0
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- Pytorch 2.0.0+cu117
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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added_tokens.json
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config.json
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{
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"_name_or_path": "openai/whisper-large-v3",
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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"apply_spec_augment": false,
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"architectures": [
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"WhisperForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"begin_suppress_tokens": [
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"bos_token_id": 50256,
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"classifier_proj_size": 256,
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"d_model": 1280,
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"decoder_attention_heads": 20,
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"decoder_ffn_dim": 5120,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 32,
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"decoder_start_token_id": 50257,
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"dropout": 0.0,
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"encoder_attention_heads": 20,
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"encoder_ffn_dim": 5120,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 32,
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"eos_token_id": 50256,
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"forced_decoder_ids": null,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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"max_source_positions": 1500,
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"max_target_positions": 448,
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"median_filter_width": 7,
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"model_type": "whisper",
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"num_hidden_layers": 32,
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"num_mel_bins": 128,
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"pad_token_id": 50256,
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"scale_embedding": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.0.dev0",
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"use_cache": true,
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"use_weighted_layer_sum": false,
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"vocab_size": 51866
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}
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{
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"fp16": {
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"enabled": "auto",
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"loss_scale": 0,
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"loss_scale_window": 1000,
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"initial_scale_power": 16,
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"hysteresis": 2,
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"min_loss_scale": 1
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},
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": "auto",
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"betas": "auto",
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"eps": "auto",
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"weight_decay": "auto"
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}
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},
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"scheduler": {
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"type": "WarmupDecayLR",
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"params": {
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"last_batch_iteration": -1,
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"total_num_steps": "auto",
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"warmup_min_lr": "auto",
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"warmup_max_lr": "auto",
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"warmup_num_steps": "auto"
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}
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},
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"zero_optimization": {
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"stage": 2,
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"offload_optimizer": {
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"device": "cpu",
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"pin_memory": true
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},
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"allgather_partitions": true,
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"allgather_bucket_size": 2e8,
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"overlap_comm": true,
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"reduce_scatter": true,
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"reduce_bucket_size": 2e8,
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"contiguous_gradients": true
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},
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"gradient_accumulation_steps": "auto",
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"gradient_clipping": "auto",
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"train_batch_size": "auto",
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"train_micro_batch_size_per_gpu": "auto"
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}
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generation_config.json
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{
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"alignment_heads": [
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[
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"bos_token_id": 50257,
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"decoder_start_token_id": 50258,
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"eos_token_id": 50257,
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"forced_decoder_ids": [
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],
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"is_multilingual": true,
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"lang_to_id": {
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"<|af|>": 50327,
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"<|am|>": 50334,
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"<|ru|>": 50263,
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"<|sa|>": 50344,
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"<|sd|>": 50332,
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"<|si|>": 50322,
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"<|sk|>": 50298,
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"<|sl|>": 50305,
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"<|sn|>": 50324,
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"<|sr|>": 50303,
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"<|su|>": 50357,
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"<|sv|>": 50273,
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"<|ta|>": 50287,
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"<|te|>": 50299,
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"<|tg|>": 50331,
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"<|th|>": 50289,
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"<|tk|>": 50341,
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"<|tl|>": 50348,
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"<|tr|>": 50268,
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"<|tt|>": 50351,
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"<|uk|>": 50280,
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"<|ur|>": 50290,
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"<|uz|>": 50337,
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"<|vi|>": 50278,
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"<|yi|>": 50335,
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"<|yo|>": 50325,
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"<|yue|>": 50358,
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"<|zh|>": 50260
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},
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"max_initial_timestamp_index": 1,
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"max_length": 448,
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"no_timestamps_token_id": 50364,
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"pad_token_id": 50257,
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"return_timestamps": false,
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"suppress_tokens": [
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|
||||||
|
25,
|
||||||
|
26,
|
||||||
|
27,
|
||||||
|
28,
|
||||||
|
29,
|
||||||
|
31,
|
||||||
|
58,
|
||||||
|
59,
|
||||||
|
60,
|
||||||
|
61,
|
||||||
|
62,
|
||||||
|
63,
|
||||||
|
90,
|
||||||
|
91,
|
||||||
|
92,
|
||||||
|
93,
|
||||||
|
359,
|
||||||
|
503,
|
||||||
|
522,
|
||||||
|
542,
|
||||||
|
873,
|
||||||
|
893,
|
||||||
|
902,
|
||||||
|
918,
|
||||||
|
922,
|
||||||
|
931,
|
||||||
|
1350,
|
||||||
|
1853,
|
||||||
|
1982,
|
||||||
|
2460,
|
||||||
|
2627,
|
||||||
|
3246,
|
||||||
|
3253,
|
||||||
|
3268,
|
||||||
|
3536,
|
||||||
|
3846,
|
||||||
|
3961,
|
||||||
|
4183,
|
||||||
|
4667,
|
||||||
|
6585,
|
||||||
|
6647,
|
||||||
|
7273,
|
||||||
|
9061,
|
||||||
|
9383,
|
||||||
|
10428,
|
||||||
|
10929,
|
||||||
|
11938,
|
||||||
|
12033,
|
||||||
|
12331,
|
||||||
|
12562,
|
||||||
|
13793,
|
||||||
|
14157,
|
||||||
|
14635,
|
||||||
|
15265,
|
||||||
|
15618,
|
||||||
|
16553,
|
||||||
|
16604,
|
||||||
|
18362,
|
||||||
|
18956,
|
||||||
|
20075,
|
||||||
|
21675,
|
||||||
|
22520,
|
||||||
|
26130,
|
||||||
|
26161,
|
||||||
|
26435,
|
||||||
|
28279,
|
||||||
|
29464,
|
||||||
|
31650,
|
||||||
|
32302,
|
||||||
|
32470,
|
||||||
|
36865,
|
||||||
|
42863,
|
||||||
|
47425,
|
||||||
|
49870,
|
||||||
|
50254,
|
||||||
|
50258,
|
||||||
|
50359,
|
||||||
|
50360,
|
||||||
|
50361,
|
||||||
|
50362,
|
||||||
|
50363
|
||||||
|
],
|
||||||
|
"task_to_id": {
|
||||||
|
"transcribe": 50360,
|
||||||
|
"translate": 50359
|
||||||
|
},
|
||||||
|
"transformers_version": "4.36.0.dev0"
|
||||||
|
}
|
||||||
50001
merges.txt
Normal file
50001
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6fd0431d832cac84fbe6f016bacfac3397c95b143d67d9e0e8a60c90402c5c27
|
||||||
|
size 3219908024
|
||||||
1742
normalizer.json
Normal file
1742
normalizer.json
Normal file
File diff suppressed because it is too large
Load Diff
14
preprocessor_config.json
Normal file
14
preprocessor_config.json
Normal file
@@ -0,0 +1,14 @@
|
|||||||
|
{
|
||||||
|
"chunk_length": 30,
|
||||||
|
"feature_extractor_type": "WhisperFeatureExtractor",
|
||||||
|
"feature_size": 128,
|
||||||
|
"hop_length": 160,
|
||||||
|
"n_fft": 400,
|
||||||
|
"n_samples": 480000,
|
||||||
|
"nb_max_frames": 3000,
|
||||||
|
"padding_side": "right",
|
||||||
|
"padding_value": 0.0,
|
||||||
|
"processor_class": "WhisperProcessor",
|
||||||
|
"return_attention_mask": false,
|
||||||
|
"sampling_rate": 16000
|
||||||
|
}
|
||||||
7
requirements.txt
Normal file
7
requirements.txt
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
datasets >= 1.18.0
|
||||||
|
git+https://github.com/huggingface/transformers
|
||||||
|
torch >= 1.5
|
||||||
|
torchaudio
|
||||||
|
librosa
|
||||||
|
jiwer
|
||||||
|
evaluate
|
||||||
39
run-stream.sh
Normal file
39
run-stream.sh
Normal file
@@ -0,0 +1,39 @@
|
|||||||
|
deepspeed run_speech_recognition_seq2seq_streaming.py \
|
||||||
|
--deepspeed="ds_config.json" \
|
||||||
|
--model_name_or_path="openai/whisper-large-v3" \
|
||||||
|
--dataset_name="mozilla-foundation/common_voice_13_0" \
|
||||||
|
--dataset_config_name="cs" \
|
||||||
|
--language="czech" \
|
||||||
|
--train_split_name="train+validation" \
|
||||||
|
--eval_split_name="test" \
|
||||||
|
--max_steps="5000" \
|
||||||
|
--output_dir="./" \
|
||||||
|
--per_device_train_batch_size="20" \
|
||||||
|
--per_device_eval_batch_size="16" \
|
||||||
|
--gradient_accumulation_steps="1" \
|
||||||
|
--logging_steps="25" \
|
||||||
|
--learning_rate="1e-6" \
|
||||||
|
--warmup_steps="500" \
|
||||||
|
--evaluation_strategy="steps" \
|
||||||
|
--eval_steps="1000" \
|
||||||
|
--save_strategy="steps" \
|
||||||
|
--save_steps="1000" \
|
||||||
|
--generation_max_length="225" \
|
||||||
|
--length_column_name="input_length" \
|
||||||
|
--max_duration_in_seconds="30" \
|
||||||
|
--text_column_name="sentence" \
|
||||||
|
--freeze_feature_encoder="False" \
|
||||||
|
--report_to="tensorboard" \
|
||||||
|
--metric_for_best_model="wer" \
|
||||||
|
--greater_is_better="False" \
|
||||||
|
--load_best_model_at_end \
|
||||||
|
--gradient_checkpointing \
|
||||||
|
--fp16 \
|
||||||
|
--overwrite_output_dir \
|
||||||
|
--do_train \
|
||||||
|
--do_eval \
|
||||||
|
--predict_with_generate \
|
||||||
|
--do_normalize_eval \
|
||||||
|
--streaming="False" \
|
||||||
|
--use_auth_token \
|
||||||
|
--push_to_hub
|
||||||
40
run.sh
Normal file
40
run.sh
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
deepspeed run_speech_recognition_seq2seq.py \
|
||||||
|
--deepspeed="ds_config.json" \
|
||||||
|
--model_name_or_path="openai/whisper-large-v3" \
|
||||||
|
--dataset_name="mozilla-foundation/common_voice_13_0" \
|
||||||
|
--dataset_config_name="cs" \
|
||||||
|
--language="czech" \
|
||||||
|
--train_split_name="train+validation" \
|
||||||
|
--eval_split_name="test" \
|
||||||
|
--max_steps="5000" \
|
||||||
|
--output_dir="./" \
|
||||||
|
--per_device_train_batch_size="62" \
|
||||||
|
--per_device_eval_batch_size="16" \
|
||||||
|
--gradient_accumulation_steps="1" \
|
||||||
|
--logging_steps="25" \
|
||||||
|
--learning_rate="1e-5" \
|
||||||
|
--warmup_steps="500" \
|
||||||
|
--evaluation_strategy="steps" \
|
||||||
|
--eval_steps="1000" \
|
||||||
|
--save_strategy="steps" \
|
||||||
|
--save_steps="1000" \
|
||||||
|
--do_lower_case="False" \
|
||||||
|
--generation_max_length="225" \
|
||||||
|
--preprocessing_num_workers="16" \
|
||||||
|
--length_column_name="input_length" \
|
||||||
|
--max_duration_in_seconds="30" \
|
||||||
|
--text_column_name="sentence" \
|
||||||
|
--freeze_feature_encoder="False" \
|
||||||
|
--report_to="tensorboard" \
|
||||||
|
--metric_for_best_model="wer" \
|
||||||
|
--greater_is_better="False" \
|
||||||
|
--load_best_model_at_end \
|
||||||
|
--gradient_checkpointing \
|
||||||
|
--group_by_length \
|
||||||
|
--fp16 \
|
||||||
|
--overwrite_output_dir \
|
||||||
|
--do_train \
|
||||||
|
--do_eval \
|
||||||
|
--predict_with_generate \
|
||||||
|
--use_auth_token \
|
||||||
|
--push_to_hub
|
||||||
625
run_speech_recognition_seq2seq.py
Normal file
625
run_speech_recognition_seq2seq.py
Normal file
@@ -0,0 +1,625 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
"""
|
||||||
|
Fine-tuning the library models for sequence to sequence speech recognition.
|
||||||
|
"""
|
||||||
|
# You can also adapt this script on your own sequence to sequence speech
|
||||||
|
# recognition task. Pointers for this are left as comments.
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import warnings
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from typing import Any, Dict, List, Optional, Union
|
||||||
|
|
||||||
|
import datasets
|
||||||
|
import evaluate
|
||||||
|
import torch
|
||||||
|
from datasets import DatasetDict, load_dataset
|
||||||
|
|
||||||
|
import transformers
|
||||||
|
from transformers import (
|
||||||
|
AutoConfig,
|
||||||
|
AutoFeatureExtractor,
|
||||||
|
AutoModelForSpeechSeq2Seq,
|
||||||
|
AutoProcessor,
|
||||||
|
AutoTokenizer,
|
||||||
|
HfArgumentParser,
|
||||||
|
Seq2SeqTrainer,
|
||||||
|
Seq2SeqTrainingArguments,
|
||||||
|
set_seed,
|
||||||
|
)
|
||||||
|
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
||||||
|
from transformers.utils import check_min_version, send_example_telemetry
|
||||||
|
from transformers.utils.versions import require_version
|
||||||
|
|
||||||
|
|
||||||
|
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
||||||
|
check_min_version("4.36.0.dev0")
|
||||||
|
|
||||||
|
require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ModelArguments:
|
||||||
|
"""
|
||||||
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
||||||
|
"""
|
||||||
|
|
||||||
|
model_name_or_path: str = field(
|
||||||
|
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
||||||
|
)
|
||||||
|
config_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
tokenizer_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
feature_extractor_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
cache_dir: Optional[str] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
|
||||||
|
)
|
||||||
|
use_fast_tokenizer: bool = field(
|
||||||
|
default=True,
|
||||||
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
||||||
|
)
|
||||||
|
model_revision: str = field(
|
||||||
|
default="main",
|
||||||
|
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
|
||||||
|
)
|
||||||
|
token: str = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
|
||||||
|
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
use_auth_token: bool = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
|
||||||
|
},
|
||||||
|
)
|
||||||
|
trust_remote_code: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
|
||||||
|
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
|
||||||
|
"execute code present on the Hub on your local machine."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
freeze_feature_encoder: bool = field(
|
||||||
|
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
|
||||||
|
)
|
||||||
|
freeze_encoder: bool = field(
|
||||||
|
default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
|
||||||
|
)
|
||||||
|
forced_decoder_ids: List[List[int]] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"A list of pairs of integers which indicates a mapping from generation indices to token indices "
|
||||||
|
"that will be forced before sampling. For example, [[0, 123]] means the first generated token "
|
||||||
|
"will always be a token of index 123."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
suppress_tokens: List[int] = field(
|
||||||
|
default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
|
||||||
|
)
|
||||||
|
apply_spec_augment: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={
|
||||||
|
"help": "Whether to apply *SpecAugment* data augmentation to the input features. This is currently only relevant for Wav2Vec2, HuBERT, WavLM and Whisper models."
|
||||||
|
},
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class DataTrainingArguments:
|
||||||
|
"""
|
||||||
|
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||||
|
"""
|
||||||
|
|
||||||
|
dataset_name: str = field(
|
||||||
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
||||||
|
)
|
||||||
|
dataset_config_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
||||||
|
)
|
||||||
|
overwrite_cache: bool = field(
|
||||||
|
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||||
|
)
|
||||||
|
preprocessing_num_workers: Optional[int] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={"help": "The number of processes to use for the preprocessing."},
|
||||||
|
)
|
||||||
|
max_train_samples: Optional[int] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"For debugging purposes or quicker training, truncate the number of training examples to this "
|
||||||
|
"value if set."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
max_eval_samples: Optional[int] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
||||||
|
"value if set."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
audio_column_name: str = field(
|
||||||
|
default="audio",
|
||||||
|
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
||||||
|
)
|
||||||
|
text_column_name: str = field(
|
||||||
|
default="text",
|
||||||
|
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
||||||
|
)
|
||||||
|
max_duration_in_seconds: float = field(
|
||||||
|
default=20.0,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
|
||||||
|
" 'max_duration_in_seconds`"
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
min_duration_in_seconds: float = field(
|
||||||
|
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
||||||
|
)
|
||||||
|
preprocessing_only: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Whether to only do data preprocessing and skip training. This is especially useful when data"
|
||||||
|
" preprocessing errors out in distributed training due to timeout. In this case, one should run the"
|
||||||
|
" preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets"
|
||||||
|
" can consequently be loaded in distributed training"
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
train_split_name: str = field(
|
||||||
|
default="train",
|
||||||
|
metadata={
|
||||||
|
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||||
|
},
|
||||||
|
)
|
||||||
|
eval_split_name: str = field(
|
||||||
|
default="test",
|
||||||
|
metadata={
|
||||||
|
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||||
|
},
|
||||||
|
)
|
||||||
|
do_lower_case: bool = field(
|
||||||
|
default=True,
|
||||||
|
metadata={"help": "Whether the target text should be lower cased."},
|
||||||
|
)
|
||||||
|
language: str = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
|
||||||
|
"only. For English speech recognition, it should be set to `None`."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
task: str = field(
|
||||||
|
default="transcribe",
|
||||||
|
metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class DataCollatorSpeechSeq2SeqWithPadding:
|
||||||
|
"""
|
||||||
|
Data collator that will dynamically pad the inputs received.
|
||||||
|
Args:
|
||||||
|
processor ([`WhisperProcessor`])
|
||||||
|
The processor used for processing the data.
|
||||||
|
decoder_start_token_id (`int`)
|
||||||
|
The begin-of-sentence of the decoder.
|
||||||
|
forward_attention_mask (`bool`)
|
||||||
|
Whether to return attention_mask.
|
||||||
|
"""
|
||||||
|
|
||||||
|
processor: Any
|
||||||
|
decoder_start_token_id: int
|
||||||
|
forward_attention_mask: bool
|
||||||
|
|
||||||
|
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
||||||
|
# split inputs and labels since they have to be of different lengths and need
|
||||||
|
# different padding methods
|
||||||
|
model_input_name = self.processor.model_input_names[0]
|
||||||
|
input_features = [{model_input_name: feature[model_input_name]} for feature in features]
|
||||||
|
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
||||||
|
|
||||||
|
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
|
||||||
|
|
||||||
|
if self.forward_attention_mask:
|
||||||
|
batch["attention_mask"] = torch.LongTensor([feature["attention_mask"] for feature in features])
|
||||||
|
|
||||||
|
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
|
||||||
|
|
||||||
|
# replace padding with -100 to ignore loss correctly
|
||||||
|
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
|
||||||
|
|
||||||
|
# if bos token is appended in previous tokenization step,
|
||||||
|
# cut bos token here as it's append later anyways
|
||||||
|
if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
|
||||||
|
labels = labels[:, 1:]
|
||||||
|
|
||||||
|
batch["labels"] = labels
|
||||||
|
|
||||||
|
return batch
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
# 1. Parse input arguments
|
||||||
|
# See all possible arguments in src/transformers/training_args.py
|
||||||
|
# or by passing the --help flag to this script.
|
||||||
|
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||||
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
|
||||||
|
|
||||||
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||||||
|
# If we pass only one argument to the script and it's the path to a json file,
|
||||||
|
# let's parse it to get our arguments.
|
||||||
|
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||||
|
else:
|
||||||
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||||
|
|
||||||
|
if model_args.use_auth_token is not None:
|
||||||
|
warnings.warn(
|
||||||
|
"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
|
||||||
|
FutureWarning,
|
||||||
|
)
|
||||||
|
if model_args.token is not None:
|
||||||
|
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
|
||||||
|
model_args.token = model_args.use_auth_token
|
||||||
|
|
||||||
|
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
|
||||||
|
# information sent is the one passed as arguments along with your Python/PyTorch versions.
|
||||||
|
send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args)
|
||||||
|
|
||||||
|
# 2. Setup logging
|
||||||
|
logging.basicConfig(
|
||||||
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||||
|
datefmt="%m/%d/%Y %H:%M:%S",
|
||||||
|
handlers=[logging.StreamHandler(sys.stdout)],
|
||||||
|
)
|
||||||
|
log_level = training_args.get_process_log_level()
|
||||||
|
logger.setLevel(log_level)
|
||||||
|
datasets.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.enable_default_handler()
|
||||||
|
transformers.utils.logging.enable_explicit_format()
|
||||||
|
|
||||||
|
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
||||||
|
|
||||||
|
# Log on each process the small summary:
|
||||||
|
logger.warning(
|
||||||
|
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
|
||||||
|
f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
|
||||||
|
)
|
||||||
|
logger.info(f"Training/evaluation parameters {training_args}")
|
||||||
|
|
||||||
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
||||||
|
if is_main_process(training_args.local_rank):
|
||||||
|
transformers.utils.logging.set_verbosity_info()
|
||||||
|
logger.info("Training/evaluation parameters %s", training_args)
|
||||||
|
|
||||||
|
# 3. Detecting last checkpoint and eventually continue from last checkpoint
|
||||||
|
last_checkpoint = None
|
||||||
|
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
||||||
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
||||||
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
||||||
|
raise ValueError(
|
||||||
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
||||||
|
"Use --overwrite_output_dir to overcome."
|
||||||
|
)
|
||||||
|
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
||||||
|
logger.info(
|
||||||
|
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
||||||
|
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
||||||
|
)
|
||||||
|
|
||||||
|
# Set seed before initializing model.
|
||||||
|
set_seed(training_args.seed)
|
||||||
|
|
||||||
|
# 4. Load dataset
|
||||||
|
raw_datasets = DatasetDict()
|
||||||
|
|
||||||
|
if training_args.do_train:
|
||||||
|
raw_datasets["train"] = load_dataset(
|
||||||
|
data_args.dataset_name,
|
||||||
|
data_args.dataset_config_name,
|
||||||
|
split=data_args.train_split_name,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
token=model_args.token,
|
||||||
|
)
|
||||||
|
|
||||||
|
if training_args.do_eval:
|
||||||
|
raw_datasets["eval"] = load_dataset(
|
||||||
|
data_args.dataset_name,
|
||||||
|
data_args.dataset_config_name,
|
||||||
|
split=data_args.eval_split_name,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
token=model_args.token,
|
||||||
|
)
|
||||||
|
|
||||||
|
if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
|
||||||
|
raise ValueError(
|
||||||
|
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
||||||
|
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
||||||
|
f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
|
||||||
|
)
|
||||||
|
|
||||||
|
if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
|
||||||
|
raise ValueError(
|
||||||
|
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
||||||
|
"Make sure to set `--text_column_name` to the correct text column - one of "
|
||||||
|
f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
|
||||||
|
)
|
||||||
|
|
||||||
|
# 5. Load pretrained model, tokenizer, and feature extractor
|
||||||
|
#
|
||||||
|
# Distributed training:
|
||||||
|
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||||
|
config = AutoConfig.from_pretrained(
|
||||||
|
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
token=model_args.token,
|
||||||
|
trust_remote_code=model_args.trust_remote_code,
|
||||||
|
)
|
||||||
|
|
||||||
|
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
|
||||||
|
|
||||||
|
# SpecAugment for whisper models
|
||||||
|
if getattr(config, "model_type", None) == "whisper":
|
||||||
|
config.update({"apply_spec_augment": model_args.apply_spec_augment})
|
||||||
|
|
||||||
|
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||||
|
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
token=model_args.token,
|
||||||
|
trust_remote_code=model_args.trust_remote_code,
|
||||||
|
)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||||||
|
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
use_fast=model_args.use_fast_tokenizer,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
token=model_args.token,
|
||||||
|
trust_remote_code=model_args.trust_remote_code,
|
||||||
|
)
|
||||||
|
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
||||||
|
model_args.model_name_or_path,
|
||||||
|
config=config,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
token=model_args.token,
|
||||||
|
trust_remote_code=model_args.trust_remote_code,
|
||||||
|
)
|
||||||
|
|
||||||
|
if model.config.decoder_start_token_id is None:
|
||||||
|
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||||||
|
|
||||||
|
if model_args.freeze_feature_encoder:
|
||||||
|
model.freeze_feature_encoder()
|
||||||
|
|
||||||
|
if model_args.freeze_encoder:
|
||||||
|
model.freeze_encoder()
|
||||||
|
model.model.encoder.gradient_checkpointing = False
|
||||||
|
|
||||||
|
if data_args.language is not None:
|
||||||
|
# We only need to set the task id when the language is specified (i.e. in a multilingual setting)
|
||||||
|
tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
|
||||||
|
|
||||||
|
# 6. Resample speech dataset if necessary
|
||||||
|
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
||||||
|
if dataset_sampling_rate != feature_extractor.sampling_rate:
|
||||||
|
raw_datasets = raw_datasets.cast_column(
|
||||||
|
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
||||||
|
)
|
||||||
|
|
||||||
|
# 7. Preprocessing the datasets.
|
||||||
|
# We need to read the audio files as arrays and tokenize the targets.
|
||||||
|
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
||||||
|
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
||||||
|
audio_column_name = data_args.audio_column_name
|
||||||
|
num_workers = data_args.preprocessing_num_workers
|
||||||
|
text_column_name = data_args.text_column_name
|
||||||
|
model_input_name = feature_extractor.model_input_names[0]
|
||||||
|
do_lower_case = data_args.do_lower_case
|
||||||
|
# if SpecAugment is used for whisper models, return attention_mask to guide the mask along time axis
|
||||||
|
forward_attention_mask = (
|
||||||
|
getattr(config, "model_type", None) == "whisper"
|
||||||
|
and getattr(config, "apply_spec_augment", False)
|
||||||
|
and getattr(config, "mask_time_prob", 0) > 0
|
||||||
|
)
|
||||||
|
|
||||||
|
if data_args.max_train_samples is not None:
|
||||||
|
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
||||||
|
|
||||||
|
if data_args.max_eval_samples is not None:
|
||||||
|
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
||||||
|
|
||||||
|
def prepare_dataset(batch):
|
||||||
|
# process audio
|
||||||
|
sample = batch[audio_column_name]
|
||||||
|
inputs = feature_extractor(
|
||||||
|
sample["array"], sampling_rate=sample["sampling_rate"], return_attention_mask=forward_attention_mask
|
||||||
|
)
|
||||||
|
# process audio length
|
||||||
|
batch[model_input_name] = inputs.get(model_input_name)[0]
|
||||||
|
batch["input_length"] = len(sample["array"])
|
||||||
|
if forward_attention_mask:
|
||||||
|
batch["attention_mask"] = inputs.get("attention_mask")[0]
|
||||||
|
|
||||||
|
# process targets
|
||||||
|
input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
|
||||||
|
batch["labels"] = tokenizer(input_str).input_ids
|
||||||
|
return batch
|
||||||
|
|
||||||
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
||||||
|
vectorized_datasets = raw_datasets.map(
|
||||||
|
prepare_dataset,
|
||||||
|
remove_columns=next(iter(raw_datasets.values())).column_names,
|
||||||
|
num_proc=data_args.preprocessing_num_workers,
|
||||||
|
desc="preprocess train dataset",
|
||||||
|
)
|
||||||
|
|
||||||
|
# filter data that is shorter than min_input_length or longer than
|
||||||
|
# max_input_length
|
||||||
|
def is_audio_in_length_range(length):
|
||||||
|
return length > min_input_length and length < max_input_length
|
||||||
|
|
||||||
|
vectorized_datasets = vectorized_datasets.filter(
|
||||||
|
is_audio_in_length_range,
|
||||||
|
num_proc=num_workers,
|
||||||
|
input_columns=["input_length"],
|
||||||
|
)
|
||||||
|
|
||||||
|
# for large datasets it is advised to run the preprocessing on a
|
||||||
|
# single machine first with `args.preprocessing_only` since there will mostly likely
|
||||||
|
# be a timeout when running the script in distributed mode.
|
||||||
|
# In a second step `args.preprocessing_only` can then be set to `False` to load the
|
||||||
|
# cached dataset
|
||||||
|
if data_args.preprocessing_only:
|
||||||
|
cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
|
||||||
|
logger.info(f"Data preprocessing finished. Files cached at {cache}.")
|
||||||
|
return
|
||||||
|
|
||||||
|
# 8. Load Metric
|
||||||
|
metric = evaluate.load("wer")
|
||||||
|
|
||||||
|
def compute_metrics(pred):
|
||||||
|
pred_ids = pred.predictions
|
||||||
|
|
||||||
|
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
||||||
|
|
||||||
|
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
||||||
|
# we do not want to group tokens when computing the metrics
|
||||||
|
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
||||||
|
|
||||||
|
wer = metric.compute(predictions=pred_str, references=label_str)
|
||||||
|
|
||||||
|
return {"wer": wer}
|
||||||
|
|
||||||
|
# 9. Create a single speech processor
|
||||||
|
# make sure all processes wait until data is saved
|
||||||
|
with training_args.main_process_first():
|
||||||
|
# only the main process saves them
|
||||||
|
if is_main_process(training_args.local_rank):
|
||||||
|
# save feature extractor, tokenizer and config
|
||||||
|
feature_extractor.save_pretrained(training_args.output_dir)
|
||||||
|
tokenizer.save_pretrained(training_args.output_dir)
|
||||||
|
config.save_pretrained(training_args.output_dir)
|
||||||
|
|
||||||
|
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
||||||
|
|
||||||
|
# 10. Define data collator
|
||||||
|
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
|
||||||
|
processor=processor,
|
||||||
|
decoder_start_token_id=model.config.decoder_start_token_id,
|
||||||
|
forward_attention_mask=forward_attention_mask,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 11. Initialize Trainer
|
||||||
|
trainer = Seq2SeqTrainer(
|
||||||
|
model=model,
|
||||||
|
args=training_args,
|
||||||
|
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
||||||
|
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
|
||||||
|
tokenizer=feature_extractor,
|
||||||
|
data_collator=data_collator,
|
||||||
|
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 12. Training
|
||||||
|
if training_args.do_train:
|
||||||
|
checkpoint = None
|
||||||
|
if training_args.resume_from_checkpoint is not None:
|
||||||
|
checkpoint = training_args.resume_from_checkpoint
|
||||||
|
elif last_checkpoint is not None:
|
||||||
|
checkpoint = last_checkpoint
|
||||||
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
||||||
|
trainer.save_model() # Saves the feature extractor too for easy upload
|
||||||
|
|
||||||
|
metrics = train_result.metrics
|
||||||
|
max_train_samples = (
|
||||||
|
data_args.max_train_samples
|
||||||
|
if data_args.max_train_samples is not None
|
||||||
|
else len(vectorized_datasets["train"])
|
||||||
|
)
|
||||||
|
metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
|
||||||
|
trainer.log_metrics("train", metrics)
|
||||||
|
trainer.save_metrics("train", metrics)
|
||||||
|
trainer.save_state()
|
||||||
|
|
||||||
|
# 13. Evaluation
|
||||||
|
results = {}
|
||||||
|
if training_args.do_eval:
|
||||||
|
logger.info("*** Evaluate ***")
|
||||||
|
metrics = trainer.evaluate(
|
||||||
|
metric_key_prefix="eval",
|
||||||
|
max_length=training_args.generation_max_length,
|
||||||
|
num_beams=training_args.generation_num_beams,
|
||||||
|
)
|
||||||
|
max_eval_samples = (
|
||||||
|
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
|
||||||
|
)
|
||||||
|
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
|
||||||
|
|
||||||
|
trainer.log_metrics("eval", metrics)
|
||||||
|
trainer.save_metrics("eval", metrics)
|
||||||
|
|
||||||
|
# 14. Write Training Stats
|
||||||
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition"}
|
||||||
|
if data_args.dataset_name is not None:
|
||||||
|
kwargs["dataset_tags"] = data_args.dataset_name
|
||||||
|
if data_args.dataset_config_name is not None:
|
||||||
|
kwargs["dataset_args"] = data_args.dataset_config_name
|
||||||
|
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
||||||
|
else:
|
||||||
|
kwargs["dataset"] = data_args.dataset_name
|
||||||
|
|
||||||
|
if training_args.push_to_hub:
|
||||||
|
trainer.push_to_hub(**kwargs)
|
||||||
|
else:
|
||||||
|
trainer.create_model_card(**kwargs)
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
629
run_speech_recognition_seq2seq_streaming.py
Normal file
629
run_speech_recognition_seq2seq_streaming.py
Normal file
@@ -0,0 +1,629 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
"""
|
||||||
|
Fine-tuning the library models for sequence to sequence speech recognition
|
||||||
|
with 🤗 Datasets' streaming mode.
|
||||||
|
"""
|
||||||
|
# You can also adapt this script for your own sequence to sequence speech
|
||||||
|
# recognition task. Pointers for this are left as comments.
|
||||||
|
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from dataclasses import dataclass, field
|
||||||
|
from typing import Any, Dict, List, Optional, Union
|
||||||
|
|
||||||
|
import datasets
|
||||||
|
import torch
|
||||||
|
from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset
|
||||||
|
from torch.utils.data import IterableDataset
|
||||||
|
|
||||||
|
import evaluate
|
||||||
|
import transformers
|
||||||
|
from transformers import (
|
||||||
|
AutoConfig,
|
||||||
|
AutoFeatureExtractor,
|
||||||
|
AutoModelForSpeechSeq2Seq,
|
||||||
|
AutoProcessor,
|
||||||
|
AutoTokenizer,
|
||||||
|
HfArgumentParser,
|
||||||
|
Seq2SeqTrainer,
|
||||||
|
Seq2SeqTrainingArguments,
|
||||||
|
TrainerCallback,
|
||||||
|
set_seed,
|
||||||
|
)
|
||||||
|
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
|
||||||
|
from transformers.trainer_pt_utils import IterableDatasetShard
|
||||||
|
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
||||||
|
from transformers.utils import check_min_version, send_example_telemetry
|
||||||
|
from transformers.utils.versions import require_version
|
||||||
|
|
||||||
|
|
||||||
|
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
|
||||||
|
check_min_version("4.25.0.dev0")
|
||||||
|
|
||||||
|
require_version("datasets>=1.18.2", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
|
||||||
|
|
||||||
|
logger = logging.getLogger(__name__)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class ModelArguments:
|
||||||
|
"""
|
||||||
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
||||||
|
"""
|
||||||
|
|
||||||
|
model_name_or_path: str = field(
|
||||||
|
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
||||||
|
)
|
||||||
|
config_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
tokenizer_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
feature_extractor_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
|
||||||
|
)
|
||||||
|
cache_dir: Optional[str] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
|
||||||
|
)
|
||||||
|
use_fast_tokenizer: bool = field(
|
||||||
|
default=True,
|
||||||
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
||||||
|
)
|
||||||
|
model_revision: str = field(
|
||||||
|
default="main",
|
||||||
|
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
|
||||||
|
)
|
||||||
|
use_auth_token: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
|
||||||
|
"with private models)."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
freeze_feature_encoder: bool = field(
|
||||||
|
default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
|
||||||
|
)
|
||||||
|
freeze_encoder: bool = field(
|
||||||
|
default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
|
||||||
|
)
|
||||||
|
forced_decoder_ids: List[List[int]] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"A list of pairs of integers which indicates a mapping from generation indices to token indices "
|
||||||
|
"that will be forced before sampling. For example, [[0, 123]] means the first generated token "
|
||||||
|
"will always be a token of index 123."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
suppress_tokens: List[int] = field(
|
||||||
|
default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
|
||||||
|
)
|
||||||
|
model_index_name: str = field(default=None, metadata={"help": "Pretty name for the model card."})
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class DataTrainingArguments:
|
||||||
|
"""
|
||||||
|
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||||
|
"""
|
||||||
|
|
||||||
|
dataset_name: str = field(
|
||||||
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
||||||
|
)
|
||||||
|
dataset_config_name: Optional[str] = field(
|
||||||
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
||||||
|
)
|
||||||
|
text_column: Optional[str] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
|
||||||
|
)
|
||||||
|
max_train_samples: Optional[int] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"For debugging purposes or quicker training, truncate the number of training examples to this "
|
||||||
|
"value if set."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
max_eval_samples: Optional[int] = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
||||||
|
"value if set."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
audio_column_name: str = field(
|
||||||
|
default="audio",
|
||||||
|
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
||||||
|
)
|
||||||
|
text_column_name: str = field(
|
||||||
|
default="text",
|
||||||
|
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
||||||
|
)
|
||||||
|
max_duration_in_seconds: float = field(
|
||||||
|
default=20.0,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Truncate audio files that are longer than `max_duration_in_seconds` seconds to"
|
||||||
|
" 'max_duration_in_seconds`"
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
min_duration_in_seconds: float = field(
|
||||||
|
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
||||||
|
)
|
||||||
|
train_split_name: str = field(
|
||||||
|
default="train",
|
||||||
|
metadata={
|
||||||
|
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||||
|
},
|
||||||
|
)
|
||||||
|
eval_split_name: str = field(
|
||||||
|
default="test",
|
||||||
|
metadata={
|
||||||
|
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
||||||
|
},
|
||||||
|
)
|
||||||
|
do_lower_case: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={"help": "Whether the target text should be lower cased."},
|
||||||
|
)
|
||||||
|
do_remove_punctuation: bool = field(
|
||||||
|
default=False,
|
||||||
|
metadata={"help": "Whether the target text should be striped of punctuation."},
|
||||||
|
)
|
||||||
|
do_normalize_eval: bool = field(
|
||||||
|
default=True,
|
||||||
|
metadata={"help": "Whether to normalise the references and predictions in the eval WER calculation."},
|
||||||
|
)
|
||||||
|
language: str = field(
|
||||||
|
default=None,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
|
||||||
|
"only. For English speech recognition, it should be set to `None`."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
task: str = field(
|
||||||
|
default="transcribe",
|
||||||
|
metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
|
||||||
|
)
|
||||||
|
shuffle_buffer_size: Optional[int] = field(
|
||||||
|
default=500,
|
||||||
|
metadata={
|
||||||
|
"help": (
|
||||||
|
"The number of streamed examples to download before shuffling them. The large the buffer, "
|
||||||
|
"the closer it is to real offline shuffling."
|
||||||
|
)
|
||||||
|
},
|
||||||
|
)
|
||||||
|
streaming: bool = field(
|
||||||
|
default=True,
|
||||||
|
metadata={"help": "Whether to use streaming mode to load and pre-process the data."},
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class DataCollatorSpeechSeq2SeqWithPadding:
|
||||||
|
"""
|
||||||
|
Data collator that will dynamically pad the inputs received.
|
||||||
|
Args:
|
||||||
|
processor ([`WhisperProcessor`])
|
||||||
|
The processor used for processing the data.
|
||||||
|
decoder_start_token_id (`int`)
|
||||||
|
The begin-of-sentence of the decoder.
|
||||||
|
"""
|
||||||
|
|
||||||
|
processor: Any
|
||||||
|
decoder_start_token_id: int
|
||||||
|
|
||||||
|
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
||||||
|
# split inputs and labels since they have to be of different lengths and need
|
||||||
|
# different padding methods
|
||||||
|
model_input_name = self.processor.model_input_names[0]
|
||||||
|
input_features = [{model_input_name: feature[model_input_name]} for feature in features]
|
||||||
|
label_features = [{"input_ids": feature["labels"]} for feature in features]
|
||||||
|
|
||||||
|
batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
|
||||||
|
|
||||||
|
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
|
||||||
|
|
||||||
|
# replace padding with -100 to ignore loss correctly
|
||||||
|
labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
|
||||||
|
|
||||||
|
# if bos token is appended in previous tokenization step,
|
||||||
|
# cut bos token here as it's append later anyways
|
||||||
|
if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
|
||||||
|
labels = labels[:, 1:]
|
||||||
|
|
||||||
|
batch["labels"] = labels
|
||||||
|
|
||||||
|
return batch
|
||||||
|
|
||||||
|
|
||||||
|
def load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train", streaming=True, **kwargs):
|
||||||
|
"""
|
||||||
|
Utility function to load a dataset in streaming mode. For datasets with multiple splits,
|
||||||
|
each split is loaded individually and then splits combined by taking alternating examples from
|
||||||
|
each (interleaving).
|
||||||
|
"""
|
||||||
|
if "+" in split:
|
||||||
|
# load multiple splits separated by the `+` symbol with streaming mode
|
||||||
|
dataset_splits = [
|
||||||
|
load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
|
||||||
|
for split_name in split.split("+")
|
||||||
|
]
|
||||||
|
# interleave multiple splits to form one dataset
|
||||||
|
interleaved_dataset = interleave_datasets(dataset_splits)
|
||||||
|
return interleaved_dataset
|
||||||
|
else:
|
||||||
|
# load a single split *with* streaming mode
|
||||||
|
dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, **kwargs)
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
# 1. Parse input arguments
|
||||||
|
# See all possible arguments in src/transformers/training_args.py
|
||||||
|
# or by passing the --help flag to this script.
|
||||||
|
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||||
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
|
||||||
|
|
||||||
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||||||
|
# If we pass only one argument to the script and it's the path to a json file,
|
||||||
|
# let's parse it to get our arguments.
|
||||||
|
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||||
|
else:
|
||||||
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||||
|
|
||||||
|
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
|
||||||
|
# information sent is the one passed as arguments along with your Python/PyTorch versions.
|
||||||
|
send_example_telemetry("run_speech_recognition_seq2seq_streaming", model_args, data_args)
|
||||||
|
|
||||||
|
# 2. Setup logging
|
||||||
|
logging.basicConfig(
|
||||||
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||||
|
datefmt="%m/%d/%Y %H:%M:%S",
|
||||||
|
handlers=[logging.StreamHandler(sys.stdout)],
|
||||||
|
)
|
||||||
|
log_level = training_args.get_process_log_level()
|
||||||
|
logger.setLevel(log_level)
|
||||||
|
datasets.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.set_verbosity(log_level)
|
||||||
|
transformers.utils.logging.enable_default_handler()
|
||||||
|
transformers.utils.logging.enable_explicit_format()
|
||||||
|
|
||||||
|
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
||||||
|
|
||||||
|
# Log on each process the small summary:
|
||||||
|
logger.warning(
|
||||||
|
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
||||||
|
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
||||||
|
)
|
||||||
|
logger.info(f"Training/evaluation parameters {training_args}")
|
||||||
|
|
||||||
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
||||||
|
if is_main_process(training_args.local_rank):
|
||||||
|
transformers.utils.logging.set_verbosity_info()
|
||||||
|
logger.info("Training/evaluation parameters %s", training_args)
|
||||||
|
|
||||||
|
# 3. Detecting last checkpoint and eventually continue from last checkpoint
|
||||||
|
last_checkpoint = None
|
||||||
|
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
||||||
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
||||||
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
||||||
|
raise ValueError(
|
||||||
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
||||||
|
"Use --overwrite_output_dir to overcome."
|
||||||
|
)
|
||||||
|
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
||||||
|
logger.info(
|
||||||
|
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
||||||
|
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
||||||
|
)
|
||||||
|
|
||||||
|
# Set seed before initializing model.
|
||||||
|
set_seed(training_args.seed)
|
||||||
|
|
||||||
|
# 4. Load dataset
|
||||||
|
raw_datasets = IterableDatasetDict() if data_args.streaming else DatasetDict()
|
||||||
|
|
||||||
|
if training_args.do_train:
|
||||||
|
raw_datasets["train"] = load_maybe_streaming_dataset(
|
||||||
|
data_args.dataset_name,
|
||||||
|
data_args.dataset_config_name,
|
||||||
|
split=data_args.train_split_name,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
streaming=data_args.streaming,
|
||||||
|
)
|
||||||
|
|
||||||
|
if training_args.do_eval:
|
||||||
|
raw_datasets["eval"] = load_maybe_streaming_dataset(
|
||||||
|
data_args.dataset_name,
|
||||||
|
data_args.dataset_config_name,
|
||||||
|
split=data_args.eval_split_name,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
streaming=data_args.streaming,
|
||||||
|
)
|
||||||
|
|
||||||
|
raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys())
|
||||||
|
|
||||||
|
if data_args.audio_column_name not in raw_datasets_features:
|
||||||
|
raise ValueError(
|
||||||
|
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
||||||
|
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
||||||
|
f"{', '.join(raw_datasets_features)}."
|
||||||
|
)
|
||||||
|
|
||||||
|
if data_args.text_column_name not in raw_datasets_features:
|
||||||
|
raise ValueError(
|
||||||
|
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
||||||
|
"Make sure to set `--text_column_name` to the correct text column - one of "
|
||||||
|
f"{', '.join(raw_datasets_features)}."
|
||||||
|
)
|
||||||
|
|
||||||
|
# 5. Load pretrained model, tokenizer, and feature extractor
|
||||||
|
#
|
||||||
|
# Distributed training:
|
||||||
|
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||||
|
config = AutoConfig.from_pretrained(
|
||||||
|
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
)
|
||||||
|
|
||||||
|
config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
|
||||||
|
|
||||||
|
if training_args.gradient_checkpointing:
|
||||||
|
config.update({"use_cache": False})
|
||||||
|
|
||||||
|
feature_extractor = AutoFeatureExtractor.from_pretrained(
|
||||||
|
model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
)
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||||||
|
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
use_fast=model_args.use_fast_tokenizer,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
)
|
||||||
|
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
||||||
|
model_args.model_name_or_path,
|
||||||
|
config=config,
|
||||||
|
cache_dir=model_args.cache_dir,
|
||||||
|
revision=model_args.model_revision,
|
||||||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||||||
|
)
|
||||||
|
|
||||||
|
if model.config.decoder_start_token_id is None:
|
||||||
|
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||||||
|
|
||||||
|
if model_args.freeze_feature_encoder:
|
||||||
|
model.freeze_feature_encoder()
|
||||||
|
|
||||||
|
if model_args.freeze_encoder:
|
||||||
|
model.freeze_encoder()
|
||||||
|
|
||||||
|
if data_args.language is not None:
|
||||||
|
# We only need to set the task id when the language is specified (i.e. in a multilingual setting)
|
||||||
|
tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
|
||||||
|
|
||||||
|
# 6. Resample speech dataset if necessary
|
||||||
|
dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
|
||||||
|
if dataset_sampling_rate != feature_extractor.sampling_rate:
|
||||||
|
raw_datasets = raw_datasets.cast_column(
|
||||||
|
data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
|
||||||
|
)
|
||||||
|
|
||||||
|
# 7. Preprocessing the datasets.
|
||||||
|
# We need to read the audio files as arrays and tokenize the targets.
|
||||||
|
max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
|
||||||
|
min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
|
||||||
|
audio_column_name = data_args.audio_column_name
|
||||||
|
text_column_name = data_args.text_column_name
|
||||||
|
model_input_name = feature_extractor.model_input_names[0]
|
||||||
|
do_lower_case = data_args.do_lower_case
|
||||||
|
do_remove_punctuation = data_args.do_remove_punctuation
|
||||||
|
normalizer = BasicTextNormalizer() # 'official' text normalizer from OpenAI
|
||||||
|
|
||||||
|
if data_args.max_train_samples is not None:
|
||||||
|
raw_datasets["train"] = (
|
||||||
|
raw_datasets["train"].take(data_args.max_train_samples)
|
||||||
|
if data_args.streaming
|
||||||
|
else raw_datasets["train"].select(range(data_args.max_train_samples))
|
||||||
|
)
|
||||||
|
|
||||||
|
if data_args.max_eval_samples is not None:
|
||||||
|
raw_datasets["eval"] = (
|
||||||
|
raw_datasets["eval"].take(data_args.max_eval_samples)
|
||||||
|
if data_args.streaming
|
||||||
|
else raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
||||||
|
)
|
||||||
|
|
||||||
|
def prepare_dataset(batch):
|
||||||
|
# process audio
|
||||||
|
sample = batch[audio_column_name]
|
||||||
|
inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
|
||||||
|
# process audio length
|
||||||
|
batch[model_input_name] = inputs.get(model_input_name)[0]
|
||||||
|
batch["input_length"] = len(sample["array"])
|
||||||
|
|
||||||
|
# process targets
|
||||||
|
input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
|
||||||
|
if do_remove_punctuation:
|
||||||
|
input_str = normalizer(input_str).strip()
|
||||||
|
batch["labels"] = tokenizer(input_str).input_ids
|
||||||
|
return batch
|
||||||
|
|
||||||
|
with training_args.main_process_first(desc="dataset map pre-processing"):
|
||||||
|
vectorized_datasets = raw_datasets.map(
|
||||||
|
prepare_dataset,
|
||||||
|
remove_columns=raw_datasets_features,
|
||||||
|
).with_format("torch")
|
||||||
|
|
||||||
|
if training_args.do_train and data_args.streaming:
|
||||||
|
# manually shuffle if streaming (done by the trainer for non-streaming)
|
||||||
|
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle(
|
||||||
|
buffer_size=data_args.shuffle_buffer_size,
|
||||||
|
seed=training_args.seed,
|
||||||
|
)
|
||||||
|
|
||||||
|
# filter training data that is shorter than min_input_length or longer than
|
||||||
|
# max_input_length
|
||||||
|
def is_audio_in_length_range(length):
|
||||||
|
return min_input_length < length < max_input_length
|
||||||
|
|
||||||
|
if training_args.do_train:
|
||||||
|
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
|
||||||
|
is_audio_in_length_range,
|
||||||
|
input_columns=["input_length"],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 8. Load Metric
|
||||||
|
metric = evaluate.load("wer")
|
||||||
|
do_normalize_eval = data_args.do_normalize_eval
|
||||||
|
|
||||||
|
def compute_metrics(pred):
|
||||||
|
pred_ids = pred.predictions
|
||||||
|
|
||||||
|
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
|
||||||
|
|
||||||
|
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
||||||
|
# we do not want to group tokens when computing the metrics
|
||||||
|
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
||||||
|
|
||||||
|
if do_normalize_eval:
|
||||||
|
pred_str = [normalizer(pred) for pred in pred_str]
|
||||||
|
label_str = [normalizer(label) for label in label_str]
|
||||||
|
# filtering step to only evaluate the samples that correspond to non-zero references:
|
||||||
|
pred_str = [pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0]
|
||||||
|
label_str = [label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0]
|
||||||
|
|
||||||
|
wer = 100 * metric.compute(predictions=pred_str, references=label_str)
|
||||||
|
|
||||||
|
return {"wer": wer}
|
||||||
|
|
||||||
|
# 9. Create a single speech processor
|
||||||
|
if is_main_process(training_args.local_rank):
|
||||||
|
# save feature extractor, tokenizer and config
|
||||||
|
feature_extractor.save_pretrained(training_args.output_dir)
|
||||||
|
tokenizer.save_pretrained(training_args.output_dir)
|
||||||
|
config.save_pretrained(training_args.output_dir)
|
||||||
|
|
||||||
|
processor = AutoProcessor.from_pretrained(training_args.output_dir)
|
||||||
|
|
||||||
|
# 10. Define data collator
|
||||||
|
data_collator = DataCollatorSpeechSeq2SeqWithPadding(
|
||||||
|
processor=processor,
|
||||||
|
decoder_start_token_id=model.config.decoder_start_token_id,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 11. Configure Trainer
|
||||||
|
# Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch
|
||||||
|
# Only required for streaming: Trainer automatically shuffles non-streaming datasets
|
||||||
|
class ShuffleCallback(TrainerCallback):
|
||||||
|
def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs):
|
||||||
|
if isinstance(train_dataloader.dataset, IterableDatasetShard):
|
||||||
|
pass # set_epoch() is handled by the Trainer
|
||||||
|
elif isinstance(train_dataloader.dataset, IterableDataset):
|
||||||
|
train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
|
||||||
|
|
||||||
|
# Initialize Trainer
|
||||||
|
trainer = Seq2SeqTrainer(
|
||||||
|
model=model,
|
||||||
|
args=training_args,
|
||||||
|
train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
|
||||||
|
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
|
||||||
|
tokenizer=feature_extractor,
|
||||||
|
data_collator=data_collator,
|
||||||
|
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
||||||
|
callbacks=[ShuffleCallback()] if data_args.streaming else None,
|
||||||
|
)
|
||||||
|
|
||||||
|
# 12. Training
|
||||||
|
if training_args.do_train:
|
||||||
|
checkpoint = None
|
||||||
|
if training_args.resume_from_checkpoint is not None:
|
||||||
|
checkpoint = training_args.resume_from_checkpoint
|
||||||
|
elif last_checkpoint is not None:
|
||||||
|
checkpoint = last_checkpoint
|
||||||
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
||||||
|
trainer.save_model() # Saves the feature extractor too for easy upload
|
||||||
|
|
||||||
|
metrics = train_result.metrics
|
||||||
|
if data_args.max_train_samples:
|
||||||
|
metrics["train_samples"] = data_args.max_train_samples
|
||||||
|
trainer.log_metrics("train", metrics)
|
||||||
|
trainer.save_metrics("train", metrics)
|
||||||
|
trainer.save_state()
|
||||||
|
|
||||||
|
# 13. Evaluation
|
||||||
|
results = {}
|
||||||
|
if training_args.do_eval:
|
||||||
|
logger.info("*** Evaluate ***")
|
||||||
|
metrics = trainer.evaluate(
|
||||||
|
metric_key_prefix="eval",
|
||||||
|
max_length=training_args.generation_max_length,
|
||||||
|
num_beams=training_args.generation_num_beams,
|
||||||
|
)
|
||||||
|
if data_args.max_eval_samples:
|
||||||
|
metrics["eval_samples"] = data_args.max_eval_samples
|
||||||
|
|
||||||
|
trainer.log_metrics("eval", metrics)
|
||||||
|
trainer.save_metrics("eval", metrics)
|
||||||
|
|
||||||
|
# 14. Write Training Stats
|
||||||
|
kwargs = {
|
||||||
|
"finetuned_from": model_args.model_name_or_path,
|
||||||
|
"tasks": "automatic-speech-recognition",
|
||||||
|
"tags": "whisper-event",
|
||||||
|
}
|
||||||
|
if data_args.dataset_name is not None:
|
||||||
|
kwargs["dataset_tags"] = data_args.dataset_name
|
||||||
|
if data_args.dataset_config_name is not None:
|
||||||
|
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
||||||
|
else:
|
||||||
|
kwargs["dataset"] = data_args.dataset_name
|
||||||
|
if "common_voice" in data_args.dataset_name:
|
||||||
|
kwargs["language"] = data_args.dataset_config_name.split('-')[0]
|
||||||
|
if model_args.model_index_name is not None:
|
||||||
|
kwargs["model_name"] = model_args.model_index_name
|
||||||
|
|
||||||
|
if training_args.push_to_hub:
|
||||||
|
trainer.push_to_hub(**kwargs)
|
||||||
|
else:
|
||||||
|
trainer.create_model_card(**kwargs)
|
||||||
|
|
||||||
|
return results
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2e2ac8a8a9e1e90d6627f516a852d953dd162e6a2676fddf5939abb3da2ef7e6
|
||||||
|
size 5161
|
||||||
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:e16cb63fb9f0e03fa4fcd079db81ecda37373375b27dcae475d73cbeb1a1ea8f
|
||||||
|
size 5007
|
||||||
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9102a6f3df4e711d987b5d35c62d3b85384b52d2f706df8d3976edb09f1804dc
|
||||||
|
size 38336
|
||||||
139
special_tokens_map.json
Normal file
139
special_tokens_map.json
Normal file
@@ -0,0 +1,139 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|startoftranscript|>",
|
||||||
|
"<|en|>",
|
||||||
|
"<|zh|>",
|
||||||
|
"<|de|>",
|
||||||
|
"<|es|>",
|
||||||
|
"<|ru|>",
|
||||||
|
"<|ko|>",
|
||||||
|
"<|fr|>",
|
||||||
|
"<|ja|>",
|
||||||
|
"<|pt|>",
|
||||||
|
"<|tr|>",
|
||||||
|
"<|pl|>",
|
||||||
|
"<|ca|>",
|
||||||
|
"<|nl|>",
|
||||||
|
"<|ar|>",
|
||||||
|
"<|sv|>",
|
||||||
|
"<|it|>",
|
||||||
|
"<|id|>",
|
||||||
|
"<|hi|>",
|
||||||
|
"<|fi|>",
|
||||||
|
"<|vi|>",
|
||||||
|
"<|he|>",
|
||||||
|
"<|uk|>",
|
||||||
|
"<|el|>",
|
||||||
|
"<|ms|>",
|
||||||
|
"<|cs|>",
|
||||||
|
"<|ro|>",
|
||||||
|
"<|da|>",
|
||||||
|
"<|hu|>",
|
||||||
|
"<|ta|>",
|
||||||
|
"<|no|>",
|
||||||
|
"<|th|>",
|
||||||
|
"<|ur|>",
|
||||||
|
"<|hr|>",
|
||||||
|
"<|bg|>",
|
||||||
|
"<|lt|>",
|
||||||
|
"<|la|>",
|
||||||
|
"<|mi|>",
|
||||||
|
"<|ml|>",
|
||||||
|
"<|cy|>",
|
||||||
|
"<|sk|>",
|
||||||
|
"<|te|>",
|
||||||
|
"<|fa|>",
|
||||||
|
"<|lv|>",
|
||||||
|
"<|bn|>",
|
||||||
|
"<|sr|>",
|
||||||
|
"<|az|>",
|
||||||
|
"<|sl|>",
|
||||||
|
"<|kn|>",
|
||||||
|
"<|et|>",
|
||||||
|
"<|mk|>",
|
||||||
|
"<|br|>",
|
||||||
|
"<|eu|>",
|
||||||
|
"<|is|>",
|
||||||
|
"<|hy|>",
|
||||||
|
"<|ne|>",
|
||||||
|
"<|mn|>",
|
||||||
|
"<|bs|>",
|
||||||
|
"<|kk|>",
|
||||||
|
"<|sq|>",
|
||||||
|
"<|sw|>",
|
||||||
|
"<|gl|>",
|
||||||
|
"<|mr|>",
|
||||||
|
"<|pa|>",
|
||||||
|
"<|si|>",
|
||||||
|
"<|km|>",
|
||||||
|
"<|sn|>",
|
||||||
|
"<|yo|>",
|
||||||
|
"<|so|>",
|
||||||
|
"<|af|>",
|
||||||
|
"<|oc|>",
|
||||||
|
"<|ka|>",
|
||||||
|
"<|be|>",
|
||||||
|
"<|tg|>",
|
||||||
|
"<|sd|>",
|
||||||
|
"<|gu|>",
|
||||||
|
"<|am|>",
|
||||||
|
"<|yi|>",
|
||||||
|
"<|lo|>",
|
||||||
|
"<|uz|>",
|
||||||
|
"<|fo|>",
|
||||||
|
"<|ht|>",
|
||||||
|
"<|ps|>",
|
||||||
|
"<|tk|>",
|
||||||
|
"<|nn|>",
|
||||||
|
"<|mt|>",
|
||||||
|
"<|sa|>",
|
||||||
|
"<|lb|>",
|
||||||
|
"<|my|>",
|
||||||
|
"<|bo|>",
|
||||||
|
"<|tl|>",
|
||||||
|
"<|mg|>",
|
||||||
|
"<|as|>",
|
||||||
|
"<|tt|>",
|
||||||
|
"<|haw|>",
|
||||||
|
"<|ln|>",
|
||||||
|
"<|ha|>",
|
||||||
|
"<|ba|>",
|
||||||
|
"<|jw|>",
|
||||||
|
"<|su|>",
|
||||||
|
"<|yue|>",
|
||||||
|
"<|translate|>",
|
||||||
|
"<|transcribe|>",
|
||||||
|
"<|startoflm|>",
|
||||||
|
"<|startofprev|>",
|
||||||
|
"<|nospeech|>",
|
||||||
|
"<|notimestamps|>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
114903
tokenizer.json
Normal file
114903
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
12996
tokenizer_config.json
Normal file
12996
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1adac70f1f1f82a14f7164910af93cebe7c58a829359d4b895c9772e839d3f49
|
||||||
|
size 5883
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user