Italian-EE-conformer

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Model Details

The model has 31,536,128 trainable parameters

Model Description

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Model trained using Early Exit architecture: 12 conformer layers, 6 CTC decoders. The model has been generated by averaging from epoch 16 to epoch 26. This model can handle only speech signals sampled at 16 kHz.

Uses

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To be used for ASR: code for using the model available at https://github.com/SpeechTechLab/early-exit-transformer

Recommendations

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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

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Common Voice (Italian) [~410h],

MultiLingual LibriSpeech (Italian) [~247h],

VoxPopuli (Italian) [~87h],

You Tube Commons (Italian) [~1580h]

Training Procedure

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47 epochs on CV followed by 80 epochs on CV+MLS+Voxpopuli followed by 5 epochs on YPT+CV+MLS+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)

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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

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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]

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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 (https://arxiv.org/abs/2309.09546)

Maxence Lasbordes, Daniele Falavigna, Alessio Brutti, “Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices”, Proc. of EUSIPCO 2025 (https://arxiv.org/abs/2506.18035)

Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna, Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients. To appear on "Progress in Artificial Intelligence" (https://arxiv.org/abs/2405.17376)

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Glossary [optional]

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