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Haopeng/libri100-ce-acoustic-frontend-20ms
libri100-ce-acoustic-frontend-20ms is a machine learning model from Haopeng. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as other.
This package contains the selected 20 ms LibriSpeech100 CE acoustic frontend. It is inference-only and does not import the IF-MDD training repo or SpeechBrain.
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Updated Jun 20, 2026
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.ckpt1.3 GB · 100%
From the Hugging Face model README
This package contains the selected 20 ms LibriSpeech100 CE acoustic frontend. It is inference-only and does not import the IF-MDD training repo or SpeechBrain.
The wrapper loads WavLM plus the small IF-MDD heads and returns:
hidden_states: 384-d acoustic hidden features.ctc_log_probs: 259-way context-phone CTC log probabilities.fused_topology_log_probs: 82-way subphonetic topology log probabilities.phone_log_probs: 41-way phone log probabilities collapsed from the fused topology head.import torch
from modeling_ifmdd_acoustic_frontend import IFMDDAcousticFrontend
model = IFMDDAcousticFrontend.from_pretrained(".", variant="20ms")
wavs = torch.randn(1, 16000) # 16 kHz mono waveform
out = model.extract(wavs)
print(out["hidden_states"].shape)
print(out["phone_log_probs"].shape)
Expected input is 16 kHz mono waveform. The wrapper does not resample audio.
The selected checkpoint is:
exp_librispeech100_topology_full_ft_ottc_triphone_pruned_budget256_film_fusedphone_ce_epoch2_step100_keepall_b32_fix1/s2_t1/wavlm_large_None_PhnMonoSSL_ContextFiLMTopology_ctc_alignment_ce/save/CKPT+globalstep000002400_epoch003_topology_PER_8.2609_MDDF1_0.0000_TSE_45.9ms_MAE_22.9ms_ACC20_0.359.ckpt
TIMIT test metrics from eval/best10ms_vs_20ms.csv:
phone_tse_ms=40.8143phone_acc_20=40.8857boundary_20_r_value_pct=76.4590