Add the Whisper large-v3 comparison with speeds

README.md

@@ -75,7 +75,25 @@ The multilingual check is nearly 6 times the size of LibriSpeech: 31.6 h over te
 | LibriSpeech test-clean, batches of 16 | 4767x | 1.93 %, unchanged |
 | Earnings-21, 44 calls (39 h), long-form | 3633x | 8.24 % |
 
-On Earnings-21, NeMo transcribing each whole call with local attention scores 9.58 %. More results, including harder English sets against Whisper large-v3, are in the [scriptrs README](https://github.com/avencera/scriptrs#cuda).
+On Earnings-21, NeMo transcribing each whole call with local attention scores 9.58 %.
+
+### Against Whisper large-v3
+
+On harder English audio, long-form with the defaults and English selected has a lower WER than Whisper large-v3 (run through WhisperX 3.8.6 in float16) on every set, and runs 10x to 14x as fast:
+
+| Set | Recordings | Hours | scriptrs WER | Whisper WER | scriptrs speed | Whisper speed | vs Whisper |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| Earnings-21 earnings calls | 44 | 39.3 | **8.2 %** | 9.7 % | **2458x** | 186x | 13.2x |
+| AMI meetings, headset mix | 34 | 18.7 | **21.5 %** | 23.8 % | **2420x** | 182x | 13.3x |
+| AMI meetings, far-field microphone | 34 | 18.7 | **23.2 %** | 28.3 % | **2414x** | 199x | 12.1x |
+| EdAcc accented conversations | 40 | 18.2 | **16.0 %** | 19.2 % | **2433x** | 201x | 12.1x |
+| CHiME-6 dinner parties | 4 | 9.7 | **44.0 %** | 44.7 % | **2438x** | 176x | 13.8x |
+| AppTek call-center calls, 14 accents | 56 | 9.4 | **11.2 %** | 12.6 % | **2220x** | 224x | 9.9x |
+| Mean over sets | 212 | 114 | **20.7 %** | 23.1 % | **2417x** | 191x | 12.6x |
+
+WER is pooled over each set's recordings, both sides through Whisper's English normalizer. Speed is x real time for transcription alone on one RTX 5090, with models loaded and audio in memory; WhisperX runs its VAD with batch size 16. The last row's speed is over all 114 hours.
+
+More results are in the [scriptrs README](https://github.com/avencera/scriptrs#cuda).
 
 ## Usage