Model Card for Model ID
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Model Details
The model has 31,536,128 trainable parameters
Model Description
Model trained using Early Exit architecture: 12 conformer layers, 6 CTC decoders. The model has been generated by averaging from epoch 5 to epoch 10.
Uses
To be used for ASR: code for using the model available at https://github.com/SpeechTechLab/early-exit-transformer
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code at https://github.com/SpeechTechLab/early-exit-transformer.
Training Details
decoder_mode='ctc', model_type='early_conformer', bpe=True
distill=False, language_model=None, language_model_dict=None, avg_model_start=0, avg_model_end=5
max_len=2000, d_model=256, n_enc_layers_per_exit=2, n_enc_exits=6, n_dec_layers=6, n_heads=8
d_feed_forward=2048, depthwise_kernel_size=31, max_utterance_length=600, sample_rate=16000
n_fft=512, win_length=320, hop_length=160, n_mels=80
src_pad_idx=0, trg_pad_idx=126, trg_sos_idx=1, trg_eos_idx=2, enc_voc_size=256, dec_voc_size=256
sp=<sentencepiece.SentencePieceProcessor=;'cv.bpe-256.model' lexicon='cv-bpe-256.lex', tokens='cv-bpe-256.tok')
Training Data
Common Voice (Italian) [~410h],
MultiLingual LibriSpeech (Italian) [~247h],
VoxPopuli (Italian) [~87h],
You Tube Commons (Italian) [~1580h]
Training Procedure
47 epochs on CV followed by 80 epochs on CV+MLS+Voxpopuli followed by 5 epochs on YPT+CV+MLX+Voxpopuli
Training Hyperparameters
shuffle=True, batch_size=64, n_batch_split=8, drop_prob=0.1, init_lr=1e-05, adam_eps=1e-09, weight_decay=0.0001, warmup=[trining dataset size], clip=1.0
Speeds, Sizes, Times [optional]
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Evaluation (%WER)
| MLS | Voxpopuli | CV |
|---|---|---|
| 19.97 | 22.26 | 23.09 |
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
FBK-digis cluster
Hardware
device=device(type='cuda', index=0, CUDA Version: 12.5) GPU quadro RTX50000
Software
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Citation [optional]
G. A. Wright, U. Cappellazzo, S. Zaiem, D. Raj, L. O. Yang, D. Falavigna, M. N. Ali, and A. Brutti, “Training early-exit architectures for automatic speech recognition: Fine-tuning pre-trained models or training from scratch,” in Proc. of ICASSP Workshops, 2024, pp. 685–689.
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