neurlang/ipa-whipstr-medium-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: 2026-07-09
• Size: 642 MB
• Total parameters:
  • Encoder: 7 237 024
  • Transformer: 44 499 744
  • Total: 51 736 768
• CER: 33.88% (66.12% success rate)
  • Note: Averaged across all supported languages, works better on higher resource languages
• WER: 73.00% (27% success rate)
  • Note: Averaged across all supported languages, works better on higher resource languages
• Training Details:
  • Hardware: Nvidia Spark
  • Batch size: 1
  • Samples: 1185000
  • Duration: 2:03:23:00
  • Runs: Jul 7 07:43 - Jul 9 11:06 (shut down at Jul 9 12:55)

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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-medium-48khz-cv-21

Inference code (microphone)

uv run --with torch --with transformers --with phase-spectrogram --with sounddevice stt_mic_hf.py --model neurlang/ipa-whipstr-medium-48khz-cv-21

Output:

config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 368/368 [00:00<00:00, 2.09MB/s]
model.safetensors: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 227M/227M [00:07<00:00, 29.4MB/s]
Loading weights: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 199/199 [00:00<00:00, 16263.01it/s]
model.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.84k/3.84k [00:00<00:00, 12.3MB/s]
Transcription: ˈɪt gˈɪt sˈʌbkəldz dˈɑɹk fˈeɪz fˈæst ðə ɹˌɛpətˈɪʃən luːp nˈoʊ ˈiːvəz bɪˈheɪvjə ɹisˈɑlɪks lˈaɪk ðə mˈɑdəl hˈæd lˈɚnd bˈeɪsɪks ˈæktɪs....

Explanaition

Generated IPA	Intended text	Assessment
ˈɪt	It	✓ Perfect
gˈɪt	gets	Very close (/ɛ/→/ɪ/, final /s/ lost)
sˈʌbkəldz	you past / past the cold-	This is where it still collapses several words together.
dˈɑɹk	start	Still confuses start with dark.
fˈeɪz	phase	✓ Excellent
fˈæst	fast	✓ Perfect
ðə	The	✓
ɹˌɛpətˈɪʃən	repetition	✓ Nearly perfect
luːp	loop	✓ Perfect
nˈoʊ	no	✓
ˈiːvəz	EOS	Surprisingly close phonetically if the model has never seen "EOS" pronounced.
bɪˈheɪvjə	behavior	✓ Excellent
ɹisˈɑlɪks	we saw looks	This is still garbled.
lˈaɪk ðə	like the	✓
mˈɑdəl	model	✓ Perfect
hˈæd	hadn't	Lost the negation.
lˈɚnd	learned	✓
bˈeɪsɪks	basic	✓
ˈæktɪs	mechanics	Still quite far.

End of training data:

Step 1185000 is chosen as this release

Step	WER	CER
986000	80.65%	36.93%
1148000	74.62%	35.12%
1185000	73.00%	33.88%
1202000	75.55%	34.24%
1218000	74.28%	35.23%