Add README describing role as semantic teacher in Higgs Audio Tokenizer

README.md

@@ -1,3 +1,57 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+  - hubert
+  - speech
+  - audio
+  - feature-extraction
+  - higgs-audio
+library_name: transformers
+pipeline_tag: feature-extraction
+---
+
+# Boson AI HuBERT Base
+
+A general-purpose HuBERT-Base checkpoint released by Boson AI, used inside the [Higgs Audio Tokenizer](https://github.com/boson-ai/higgs-audio) as the semantic teacher.
+
+## What it is
+
+- Standard HuBERT-Base architecture (12 transformer layers, hidden size 768, ~95M params)
+- 16 kHz audio input
+- Loadable via `AutoModel` with `trust_remote_code=True`
+- Outputs 768-dim per-layer hidden states (`output_hidden_states=True`)
+
+## How it is used in Higgs Audio
+
+The Higgs Audio Tokenizer distills semantic features from this HuBERT into its semantic branch. From [`boson_multimodal/audio_processing/higgs_audio_tokenizer.py`](https://github.com/boson-ai/higgs-audio/blob/main/boson_multimodal/audio_processing/higgs_audio_tokenizer.py) (`semantic_techer="hubert_base_general"`):
+
+```python
+from transformers import AutoModel
+
+semantic_model = AutoModel.from_pretrained("bosonai/hubert_base", trust_remote_code=True)
+# 16 kHz, 768-dim semantic features, all hidden layers consumed by the tokenizer
+```
+
+## Direct usage
+
+```python
+import torch
+import torchaudio
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("bosonai/hubert_base", trust_remote_code=True).eval()
+
+waveform, sr = torchaudio.load("audio.wav")
+if sr != 16000:
+    waveform = torchaudio.functional.resample(waveform, sr, 16000)
+
+with torch.no_grad():
+    out = model(waveform, output_hidden_states=True)
+
+# out.last_hidden_state:  (B, T, 768)
+# out.hidden_states:      tuple of (B, T, 768) for each of the 13 layers (embedding + 12 transformer blocks)
+```
+
+## License
+
+Apache 2.0.