Dasheng Base Env Encoder

--- license: apache-2.0 base_model: mispeech/dasheng-base --- # dasheng-base-env-encoder `mispeech/dasheng-base` fine-tuned (LoRA, merged) to extract environment/background embeddings from speech. Trained on `ChristianYang/Env-TTS-Clean` with conversation-level AAMSoftmax. Auto-uploaded from training step **100000**. ## Usage ```python import torch from transformers import AutoModel, AutoFeatureExtractor from huggingface_hub import hf_hub_download repo = "ChristianYang/dasheng-base-env-encoder" backbone = AutoModel.from_pretrained(repo, trust_remote_code=True).eval() fe = AutoFeatureExtractor.from_pretrained(repo, trust_remote_code=True) head = torch.load(hf_hub_download(repo, "head.pt"), map_location="cpu", weights_only=True) # pipeline: 16 kHz wav -> fe -> mel -> backbone.encoder tokens [B,T,768] # -> Conv1d x2 (head["cnn"]) -> masked attentive pooling (head["pool"]) -> 768-d embedding ``` Head weights (`head.pt`): two Conv1d(768,768,k=3) layers + attentive pooling (Linear(768,1)). Token count = mel frames // 4.