Ming-3.0 speech evaluation
inclusionAI reports 0.800% Chinese and 1.584% English word error rate on the Seed-TTS testset for Ming-3.0-tts-0.5B, and 3.45 Chinese CER with 3.69 English WER for Ming-3.0-audio-flash.
Reasoning, task completion, conversation dynamics, experience, and latency stay separate.
Benchmark profile
- Scope
- Zero-shot, multilingual, paralinguistic, dialect, emotion and podcast text-to-speech; Chinese, English, dialect, context and long-form speech recognition; audio understanding, spoken interaction and tool calling
- Primary metric
- Word and character error rates (%, lower is better); accuracy, similarity and benchmark scores (higher is better)
- Owner
- inclusionAI
- Available evidence
- Exact values in the Hugging Face model cards; the audio and ASR cards publish them as table images
What it measures
Available results
Zero-shot text to speech on the Seed-TTS testset
inclusionAI-reported. WER is lower-is-better; SIM (speaker similarity) is higher-is-better.
| Model | Chinese WER | Chinese SIM | English WER | English SIM |
|---|---|---|---|---|
| Ming-3.0-tts-0.5B | 0.800% | 0.758 | 1.584% | 0.691 |
| Ming-3.0-tts-tiny | 0.820% | 0.781 | 1.666% | 0.728 |
Multilingual text to speech
inclusionAI-reported on the MiniMax TTS-Multilingual test set. German, Spanish and French use WER; Korean and Japanese use CER; each is followed by SIM.
| Model | German WER | German SIM | Spanish WER | Spanish SIM | French WER | French SIM | Korean CER | Korean SIM | Japanese CER | Japanese SIM |
|---|---|---|---|---|---|---|---|---|---|---|
| Ming-3.0-tts-0.5B | 0.365% | 0.753 | 1.099% | 0.763 | 4.264% | 0.675 | 1.728% | 0.793 | 3.765% | 0.765 |
| Ming-3.0-tts-tiny | 0.325% | 0.791 | 0.971% | 0.797 | 3.470% | 0.733 | 6.000% | 0.797 | 3.427% | 0.780 |
Style, dialect, emotion and podcast text to speech
inclusionAI-reported. Dialect CER is lower-is-better; the other columns are higher-is-better except podcast CER. The podcast test covers the tiny model only.
| Model | InstructTTSEval-ZH voice design | Basic attribute control | CV3 emotion control | WSC-Eval-TTS-easy CER | WSC-Eval-TTS-hard CER | Podcast CER |
|---|---|---|---|---|---|---|
| Ming-3.0-tts-0.5B | 72.29% | 93.30% | 72.33% | 2.27% | 3.35% | Not reported |
| Ming-3.0-tts-tiny | 75.37% | 88.23% | 74.00% | 2.53% | 3.11% | 2.03% |
Speech recognition
inclusionAI-reported. Chinese and dialect averages are CER; English averages and the context and long-form columns are WER; lower is better. Read from images on the model cards.
| Model | Chinese average CER | English average WER | Dialect average CER | ContextASR WER | Earnings22 WER |
|---|---|---|---|---|---|
| Ming-3.0-audio-flash | 3.45 | 3.69 | 9.40 | 1.52 | 11.60 |
| Ming-3.0-audio-tiny | 3.79 | 4.36 | 10.29 | 2.12 | 18.29 |
| Ming-3.0-asr-tiny | 3.55 | 3.99 | Not reported | Not reported | Not reported |
Audio understanding and spoken interaction
inclusionAI-reported; higher is better. Read from images on the audio model cards. The ASR model does not report these.
| Model | BigBenchAudio | MMAU | MMAR | MMSU | MMAU-Pro | VoiceBench | URO-Bench-Pro | BFCL-v3 Audio | SpeechFCEval |
|---|---|---|---|---|---|---|---|---|---|
| Ming-3.0-audio-flash | 99.40 | 82.00 | 74.40 | 78.82 | 66.41 | 86.74 | 81.92 | 81.40 | 82.60 |
| Ming-3.0-audio-tiny | 99.20 | 75.90 | 67.70 | 73.06 | 56.42 | 74.29 | 54.15 | 79.80 | 69.80 |