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whettenr/asr-fon-streaming-conformer-without-diacritics

sourceHugging Facecc-by-nc-4.0updated 9mo agoView on Hugging Face

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Fongbe ASR model w/out diacritics

How to use for inference

python

from speechbrain.inference.ASR import StreamingASR
from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig

asr_model = StreamingASR.from_hparams(
    source="whettenr/asr-fon-streaming-conformer-without-diacritics",
    savedir="pretrained_models/asr-fon-streaming-conformer-without-diacritics"
)

asr_model.transcribe_file(
    "whettenr/asr-fon-without-diacritics/example.wav",
    # select a chunk size of ~960ms with 4 chunks of left context
    DynChunkTrainConfig(24, 8),
    # disable torchaudio streaming to allow fetching from HuggingFace
    # set this to True for your own files or streams to allow for streaming file decoding
    use_torchaudio_streaming=False,
)

# expected output:
# huzuhuzu gɔngɔn ɖe ɖo dandan

Details of model

~100M parameters, 12 layer conformer encoder, Transducer (LSTM) decoder

Details of training

—pretrained using BEST-RQ on 700 hours for 400k steps
—140 hours of Fongbé from:
—FFSTC 2 + beethogedeon/fongbe-speech (~40 hours)
—cappfm (~100 hours)
—140 hours of English and French (from Librispeech)
—140 hours of Hausa and Yoruba from VoxLingua107,CommonVoice 23.0 and BibleTTS

—finetuned with Transducer decoder loss on training sets of
—FFSTC 2
—beethogedeon/fongbe-speech
—a small portion of automatically generated transcriptions from cappfm audio
—Sentence Piece BPE (set to 100)

# other citation coming soon

# dataset citation
@inproceedings{kponou25_interspeech,
  title     = {{Extending the Fongbe to French Speech Translation Corpus:  resources, models and benchmark}},
  author    = {D. Fortuné Kponou and Salima Mdhaffar and Fréjus A. A. Laleye and Eugène C. Ezin and Yannick Estève},
  year      = {2025},
  booktitle = {{Interspeech 2025}},
  pages     = {4533--4537},
  doi       = {10.21437/Interspeech.2025-1801},
  issn      = {2958-1796},
}