Text-to-speech (TTS) turns written text into spoken audio. For this comparison, the practical difference is that Ming-3.0-tts-0.5B is a downloadable checkpoint with a separate inference-code repository, while Alibaba Cloud describes Qwen-Audio-3.1-TTS-Flash as real-time streaming synthesis with system voices.
Ming's checkpoint needs separate inference code
Ming-3.0-tts-0.5B is a dense model with 0.5 billion parameters, built on Qwen2.5-0.5B and released under the MIT license. A checkpoint is the saved model package; inference code runs it to generate audio. The Ming model card says Hugging Face distributes the checkpoint, while inference code and current dependencies are in the separate Ming-3.0-tts GitHub repository. Its listed audio sample rate is 44.1 kHz.
The card lists zero-shot voice cloning from a short reference clip, voice design from a natural-language description, and controls for delivery, emotion, speed, pitch, and volume. It also lists more than 100 built-in Chinese voices, five English voices, and 14 inline tags for effects such as laughter, hesitation, and surprise.
What Ming's setup instructions specify
The model card reports testing on NVIDIA H800-80GB and H20-96G configurations with CUDA 12.4. These are tested configurations, not stated minimum hardware requirements. flash-attn is optional; the instructions say inference can fall back to PyTorch SDPA.
For FST-based normalization of numbers, dates, units, and symbols, the instructions call for pynini. They also provide a --no_text_normalization option to skip that normalization. These details help separate the model download from the software and hardware choices involved in running it.
Spanish support and the scores in Ming's model card
The model card lists seven languages: Chinese, English, German, Spanish, French, Korean, and Japanese. For Spanish synthesis with the MULTILINGUAL task, it requires a language_id. The card also lists Sichuanese and Cantonese dialect support.
For the 0.5B checkpoint, inclusionAI reports a Spanish word error rate (WER) of 1.099% and a speaker-similarity score (SIM) of 0.763 on its selected MiniMax multilingual test set. WER measures word errors in a transcription evaluation; lower is better. These are inclusionAI's published evaluation figures, not a shared Ming-versus-Qwen result.
The same card says speaker similarity trails content accuracy, French is its clearest multilingual similarity gap, and stability with several delivery or emotion cues in one sentence is still improving. It also describes pitch control as less accurate than speed and volume. Those qualifications matter if voice resemblance or precise prosody, the rhythm and intonation of speech, is central to your use case.
What Alibaba documents for Qwen-Audio-3.1-TTS-Flash
Alibaba Cloud Model Studio describes Qwen-Audio-3.1-TTS-Flash as a real-time speech-synthesis model that supports streaming and offers system voices. That is the documented Qwen capability to weigh when a workflow calls for streaming output or built-in voices; Ming's card instead gives developers a checkpoint, a separate inference repository, and setup details.
