# Minimax Speech Eval

> Evaluates zero-shot and one-shot text-to-speech voice cloning fidelity, multilingual synthesis capability, and cross-lingual generalization. It measures perceptual naturalness and speaker identity preservation through objective transcription and embedding similarity metrics, alongside human preference rankings. Use when the user wants to benchmark on Seed-TTS-eval, Artificial Arena, MiniMax Multilingual Test Set, or asks about evaluating this task. Reports WER, SIM.

- Skill: `qhjqhj00/minimax-speech-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/minimax-speech-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/minimax-speech-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/minimax-speech-eval

---


# minimax-speech-eval

> MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder — Bowen Zhang et al. (2025) (arXiv:2505.07916, 2025)

## What this evaluates

Evaluates zero-shot and one-shot text-to-speech voice cloning fidelity, multilingual synthesis capability, and cross-lingual generalization. It measures perceptual naturalness and speaker identity preservation through objective transcription and embedding similarity metrics, alongside human preference rankings.

## Datasets

- **Seed-TTS-eval** — total 3000; splits: test-zh (2000), test-en (1000)
- **Artificial Arena** — total ?; splits: test (-1); repo https://artificialanalysis.ai
- **MiniMax Multilingual Test Set** — total 2400; splits: test (2400)

## Metrics

- `WER` **(primary)** — range: percent
  - Word Error Rate computed by transcribing synthesized audio with Whisper-large-v3 (English) or Paraformer-zh (Chinese) and comparing against ground-truth text.
- `SIM` **(primary)** — range: [0, 1]
  - Cosine similarity between speaker embeddings extracted from reference and synthesized audio using a WavLM-large model fine-tuned for speaker verification.
- `ELO` — range: other
  - Dynamic ranking score derived from pairwise human preference judgments on the Artificial Arena leaderboard.

## Input / output format

**Input**: Target text string and a reference audio clip (untranscribed for zero-shot, optionally transcribed/prompted for one-shot).

**Output**: Synthesized audio waveform corresponding to the target text.

## Scoring recipe

```python
# WER
asr = WhisperLargeV3() if lang == 'en' else ParaformerZh()
pred_text = asr.transcribe(generated_audio)
wer = compute_wer(pred_text, ground_truth_text)

# SIM
encoder = WavLMLargeFineTuned()
ref_emb = encoder.encode(reference_audio)
gen_emb = encoder.encode(generated_audio)
sim = cosine_similarity(ref_emb, gen_emb)

# ELO
elo = artificial_arena_leaderboard.get_elo(model_name)
```

## Common pitfalls

- WER values are highly dependent on the specific ASR model used for transcription (Whisper-large-v3 vs Paraformer-zh), making cross-paper comparisons difficult.
- SIM scores use a custom fine-tuned WavLM-large speaker verification model, not the standard pre-trained WavLM, so results will not match baseline WavLM similarity scores.
- Artificial Arena ELO scores are dynamic and context-dependent, changing as new models are added or removed from the leaderboard.

## Evidence (verbatim from paper)

> The fidelity of voice cloning was quantitatively assessed using WER and SIM metrics on the Seed-TTS-eval (Anastassiou et al., 2024) test set. This dataset comprises two distinct subsets: test-zh (approximately 2,000 Chinese samples) and test-en (approximately 1,000 English samples). Each sample in these subsets includes a reference audio and a corresponding ground-truth audio from the identical speaker. For WER computation, synthesized English and Chinese audio were transcribed using Whisper-large-v3 (Radford et al., 2023) and Paraformer-zh (Gao et al., 2023b), respectively. SIM was determined by calculating the cosine similarity between speaker embeddings, which were extracted using a speaker verification model fine-tuned on WavLM-large.

## Citation

```bibtex
@misc{zhang2025minimaxspeech,
  title={MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder},
  author={Bowen Zhang et al. (2025)},
  year={2025},
  note={arXiv:2505.07916}
}
```

- arXiv: 2505.07916