English-EE-conformer

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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 60 to epoch 90. This model can handle only speech signals sampled at 16 kHz.

Uses

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

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=60, avg_model_end=90

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=;'aiXpa_en.bpe-256.model' lexicon='aiXpa_en-bpe-256.lex', tokens='aiXpa_en-bpe-256.tok')

Training Data

LibriSpeech, Voxpopuli, TEDLIUM release 3

Training Procedure

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From scratch

Preprocessing [optional]

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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

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Evaluation (%WER)

Testing Data, Factors & Metrics

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Results

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Summary

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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]

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.

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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