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* initial commit for SPGISpeech recipe * add decoding * add spgispeech transducer * remove conformer ctc; minor fixes in RNN-T * add results * add tensorboard * add pretrained model to HF * remove unused scripts and soft link common scripts * remove duplicate files * pre commit hooks * remove change in librispeech * pre commit hook * add CER numbers
33 lines
1.6 KiB
Markdown
33 lines
1.6 KiB
Markdown
# SPGISpeech
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SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective
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transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in
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length to allow easy training for speech recognition systems. Calls represent a broad
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cross-section of international business English; SPGISpeech contains approximately 50,000
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speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and
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L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio.
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Transcription text represents the output of several stages of manual post-processing.
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As such, the text contains polished English orthography following a detailed style guide,
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including proper casing, punctuation, and denormalized non-standard words such as numbers
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and acronyms, making SPGISpeech suited for training fully formatted end-to-end models.
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Official reference:
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O’Neill, P.K., Lavrukhin, V., Majumdar, S., Noroozi, V., Zhang, Y., Kuchaiev, O., Balam,
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J., Dovzhenko, Y., Freyberg, K., Shulman, M.D., Ginsburg, B., Watanabe, S., & Kucsko, G.
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(2021). SPGISpeech: 5, 000 hours of transcribed financial audio for fully formatted
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end-to-end speech recognition. ArXiv, abs/2104.02014.
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ArXiv link: https://arxiv.org/abs/2104.02014
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## Performance Record
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| Decoding method | val WER | val CER |
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|---------------------------|------------|---------|
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| greedy search | 2.40 | 0.99 |
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| modified beam search | 2.24 | 0.91 |
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| fast beam search | 2.35 | 0.97 |
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See [RESULTS](/egs/spgispeech/ASR/RESULTS.md) for details.
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