Text-to-Audio
Transformers
Safetensors
midashenglm-gen
feature-extraction
audio-generation
flow-matching
dasheng
custom_code
Instructions to use mispeech/midashenglm-gen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/midashenglm-gen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="mispeech/midashenglm-gen", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mispeech/midashenglm-gen", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "midashenglm-gen", | |
| "architectures": [ | |
| "MiDashengLMGenForAudioGeneration" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_midashenglm.MiDashengLMGenConfig", | |
| "AutoModel": "modeling_midashenglm.MiDashengLMGenForAudioGeneration" | |
| }, | |
| "seq_len": 500, | |
| "audio_emb_size": 768, | |
| "llm_emb_dim": 2048, | |
| "target_emb_dim": 768, | |
| "flow_dit_width": 2048, | |
| "flow_dit_depth": 16, | |
| "flow_dit_heads": 8, | |
| "flow_dit_mlp_ratio": 4.0, | |
| "cfg_trainable": true, | |
| "llm_model": "Qwen/Qwen3-1.7B", | |
| "eval_cfg": 2.0, | |
| "stop_threshold": 0.5, | |
| "min_stop_step": 5, | |
| "sample_rate": 16000, | |
| "torch_dtype": "float32" | |
| } |