ipa-whipstr-base-48khz-cv-21

Neurlang Whipstr STT (ASR)

A deep learning automatic speech recognition (ASR) system for transcribing speech audio into IPA text using transformer-based sequence-to-sequence models.

• Language: Universal (IPA), 74+ languages
• Model Github: neurlang/whipstr https://github.com/neurlang/whipstr
• Model Dataset: Common Voice 21
• Model-Native Sample Rates: 8000 Hz, 16000 Hz, 24000 Hz, 32000 Hz, 48000 Hz
• Degraded-Performance Sample Rates: 11025 Hz, 22050 Hz, 44100 Hz
• License: GPL v2
• Release: 2026-07-07
• Size: 186 MB
• Total parameters:
  • Encoder: 7 220 576
  • Transformer: 7 537 184
  • Total: 14 757 760
• CER: 48.68% (51.32% success rate)
  • Note: Averaged across all supported languages, works better on higher resource languages
• WER: 92.58% (7.42% success rate)
  • Note: Averaged across all supported languages, works better on higher resource languages
• Training Details:
  • Hardware: Nvidia Spark
  • Batch size: 1
  • Samples: 960000
  • Duration: 1:11:10:00
  • Runs: Jul 5 14:58 - Jul 5 17:45, Jul 5 22:41 - Jul 6 04:38, Jul 6 04:46 - Jul 7 07:12 (shut down at Jul 7 07:36)

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Inference code

git clone https://github.com/neurlang/whipstr.git
cd whipstr/
uv run --with torch --with transformers --with phase-spectrogram stt_infer_hf.py --audio /home/m/Downloads/LJ001-0001.wav --model neurlang/ipa-whipstr-base-48khz-cv-21

Output:

Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 139/139 [00:00<00:00, 13371.75it/s]
Transcription: ˈɪt ˈæt juː pˈæst ðə kˈɔldz tˈɑɹk fˈeɪs vˈæst ðə ɹˈɛpɪtʃən lˈupənəl ˈi ˈæz bɪhˈeɪviɚ wˈi sˈɑloʊks lˈɛkəmˌɑɹəl hˈæn jˈæt lˈɚnd bˈeɪsɪks sˈɛktɪs ˈækmɪkmɪkəɹɪkənɪkəksɪksɪksɪksɪksɪksɪksɪksɪkstəkstəksɪksɪk

Explaination:

End of training data:

Step 960000 is chosen as this release