Replace talker-9500 with GRPO checkpoint-160 and repository model card

.gitattributes

@@ -34,3 +34,10 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/architecture.png filter=lfs diff=lfs merge=lfs -text
+assets/results.png filter=lfs diff=lfs merge=lfs -text
+assets/training.png filter=lfs diff=lfs merge=lfs -text
+results_backchannel.png filter=lfs diff=lfs merge=lfs -text
+results_groundedness.png filter=lfs diff=lfs merge=lfs -text
+results_logic.png filter=lfs diff=lfs merge=lfs -text
+results_qa.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,46 +1,256 @@
 ---
 language:
-  - en
-  - zh
-tags:
-  - speech-to-speech
-  - full-duplex
-  - awaken-chat
+- en
+license: apache-2.0
+base_model: FunAudioLLM/Fun-Audio-Chat-8B
 pipeline_tag: audio-to-audio
+tags:
+- full-duplex
+- speech-to-speech
+- spoken-dialogue
+- audio-language-model
 ---
-# Awaken Chat — talker checkpoint 9500
 
-Full-duplex speech-to-speech checkpoint from `podcast-talker-v3-micro-b64-2ep/checkpoint-9500`.
-Weights are the original checkpoint shards. A model-weight license has not been specified;
-the inference code repository's Apache-2.0 license does not establish the license of these weights.
+# Awaken Chat
 
-## Load with the inference repository
+<div align="center">
 
-Use [OpenmartAI/awake-audio-chat](https://github.com/OpenmartAI/awake-audio-chat),
-with its documented CUDA / PyTorch dependencies and Transformers 4.52.x:
+**Awaken: An Efficient Training Recipe for Full-Duplex Conversation That Retains Model Ability**
 
-```python
-from awaken_chat import load_model
+[![Blog](https://img.shields.io/badge/Blog-Project%20Page-orange)](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/OpenmartAI/awake-audio-chat)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Model-yellow)](https://huggingface.co/openmartdev/awaken-full-duplex)
+[![Base model](https://img.shields.io/badge/Base-Fun--Audio--Chat--8B-green)](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B)
+
+</div>
+
+## Model Description
+
+Full-duplex voice models can listen and speak at the same time, so they can acknowledge,
+interrupt, and hand over turns the way people do. Teaching this usually depends on natural
+conversational audio rich in backchannels. That audio is scarce, and without careful curation
+it can weaken a model's reasoning and grounding. **Awaken** learns full-duplex behavior from a
+small amount of such data and keeps its base model's scores.
+
+<p align="center">
+  <img width="95%" src="assets/results.png" alt="Awaken evaluation results: backchannels, groundedness, spoken logic puzzles, and spoken question answering">
+</p>
+
+### Key Features
+
+- **Efficient**: Starts from the half-duplex [Fun-Audio-Chat-8B](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B)
+  checkpoint and teaches turn-taking with composed conversations that have explicit timing,
+  overlap, and interruptions. Two supervised stages and a short reinforcement-learning stage
+  are enough; there is no large-scale duplex pretraining.
+- **Effective**: A targeted GRPO stage raises the backchannel pass rate from 1% (supervised
+  training alone) to **63%**, while taking the floor in only **3%** of items.
+- **Retains model ability**: Passes **61%** of groundedness probes, near its half-duplex base
+  (66%) and well above open full-duplex models (14–35%). Solves **90%** of spoken logic
+  puzzles, compared with 30–40% for open full-duplex models.
+
+<p align="center">
+  <img width="95%" src="assets/architecture.png" alt="Awaken architecture: user features, text embeddings, and audio-token embeddings enter a shared fusion layer and transformer backbone that predicts the next text token and conditions the audio decoder">
+</p>
+
+### Training Recipe
+
+<p align="center">
+  <img width="80%" src="assets/training.png" alt="Training path from Fun-Audio-Chat through streaming adaptation, two SFT stages, and backchannel-focused GRPO">
+</p>
+
+1. **Stage 1 SFT**: learns the streaming input pattern and aligned text and speech targets on
+   64,000 composed conversations (windows up to 180 s).
+2. **Stage 2 SFT**: blends the stage-1 weights 50/50 with the base checkpoint, then continues
+   on 100,132 records with replay of the interaction data.
+3. **GRPO**: improves backchannel timing from the model's own samples in conversational windows.
+
+See the [project page](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html) for the full data composition and training details.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| Model Size | ~8B parameters |
+| Base Model | [Fun-Audio-Chat-8B](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B) (half-duplex) |
+| Backbone | Qwen3-8B, one position per 200 ms frame |
+| Input | Streaming 24 kHz user audio through a causal Whisper-style encoder (2 s attention window) |
+| Output | Text tokens and 5 codec tokens per frame, rendered by the CosyVoice3 flow decoder and HiFT vocoder |
+| Interaction | Full-duplex: no turns, no voice-activity detector |
+| Languages | English |
+| License | Apache 2.0 (same as the base model) |
+
+## Requirements
 
-loaded = load_model("openmartdev/awaken-full-duplex")
+- Linux with an NVIDIA GPU (CUDA)
+- Python >= 3.10
+- PyTorch >= 2.7 (tested with 2.8, CUDA 12.8)
+- TensorRT 10
+
+## Installation
+
+```bash
+git clone https://github.com/OpenmartAI/awake-audio-chat
+cd awake-audio-chat
+
+# 1. torch + torchaudio for YOUR CUDA driver first (the default PyPI wheel targets the newest
+#    CUDA and will not load on older drivers), e.g. for CUDA 12.8:
+pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
+# 2. the package
+pip install -e .            # Linux + CUDA GPU; pulls transformers, tensorrt, ...
+pip install -e ".[fast]"    # + flash-attn (faster decode; optional)
+pip install -e ".[voice]"   # + onnxruntime, to make voice prompts from a clip
 ```
 
+## Quick Start
+
+### Download Models
+
 ```bash
-awaken-server --model openmartdev/awaken-full-duplex --port 8998
+pip install huggingface-hub
+hf download openmartdev/awaken-full-duplex --local-dir ./pretrained_models/awaken-chat
 ```
 
-The weights require `AwakenModel` from that repository. Generic Transformers
-`AutoModel.from_pretrained` is not a supported loading path for this prepared directory.
-The server separately downloads CosyVoice3 `flow.pt`, `hift.pt` and the ONNX estimator
-from `FunAudioLLM/Fun-CosyVoice3-0.5B-2512`; GPU-specific TensorRT plans are built locally.
-Optional voice prompts are distributed with the inference repository.
+The speech codec is fetched automatically from
+[`FunAudioLLM/Fun-CosyVoice3-0.5B-2512`](https://huggingface.co/FunAudioLLM/Fun-CosyVoice3-0.5B-2512)
+(flow, vocoder and the estimator's ONNX export only). The TensorRT engine is built once per
+machine on first use, which takes a few minutes.
+
+### Inference
+
+Live server (one conversation at a time, WebSocket protocol in `awaken_chat/server/protocol.py`):
+
+```bash
+awaken-server --model ./pretrained_models/awaken-chat --port 8998 [--ssl-dir certs] [--voices-dir assets/voices]
+```
+
+Offline, with a recorded user track:
+
+```bash
+awaken-offline --model ./pretrained_models/awaken-chat --input user.wav --out out/   # stereo.wav, transcript.txt, tokens.pt
+```
+
+Make a voice prompt from a reference clip (3-10 s of one speaker you have the rights to):
+
+```bash
+awaken-voice --clip speaker.wav --out assets/voices/speaker.pt
+```
+
+From Python:
+
+```python
+from awaken_chat import load_model, Decoders, DuplexSession, system_prompt
+
+loaded = load_model("./pretrained_models/awaken-chat")   # HF repo id or local directory
+decoders = Decoders(loaded.model)                         # static caches + CUDA graphs, once per process
+session = DuplexSession(loaded.model, decoders, loaded.prompt_ids(system_prompt("You are a tour guide.")))
+for features in ear_frames:                               # (5, 4096) per 200 ms, see models/ear.py
+    out = session.step(features)                          # out.text_id, out.audio_ids, out.state
+```
+
+## Evaluation
+
+We measure listener behavior, following the conversation, and knowledge and reasoning side by
+side. Awaken and Fun-Audio-Chat-8B are run by us; other rows come from the public
+InteractionBench results and the Fun-Audio-Chat technical report.
+
+| System | Backchannel pass ↑ | Floor-take ↓ | Groundedness probes ↑ | Logic puzzles ↑ | Llama Q. ↑ | TriviaQA ↑ |
+|--------|-------------------:|-------------:|----------------------:|----------------:|-----------:|-----------:|
+| GPT-Realtime | 10% | 5% | 67% | 100% | – | – |
+| Moshi | 78% | 22% | 14% | 30% | 65.67 | 18.46 |
+| Freeze-Omni | 0% | 39% | 35% | 40% | 74.00 | 30.08 |
+| PersonaPlex | 83% | 11% | 30% | 30% | – | – |
+| Fun-Audio-Chat-Duplex-8B | – | – | – | – | 72.33 | 29.59 |
+| Fun-Audio-Chat-8B (half-duplex base) | n/a | n/a | 66% | 100% | 76.2 | 48.7 |
+| Awaken SFT | 1% | 6% | 62% | 87% | 70.7 | 35.5 |
+| **Awaken GRPO** | **63%** | **3%** | **61%** | **90%** | **73.4** | **36.2** |
+
+- **Backchannels**: 30 InteractionBench storytelling items × 3 seeds. An item passes when the
+  assistant backchannels at least once and never takes the floor. A half-duplex model cannot
+  speak while the user is speaking, so the base model is n/a.
+- **Groundedness**: 10 conversations of 22–24 turns, then 52 probes (recall, updates, unknown
+  information, constraints, false premises, nonsense questions). Our rows are means over 5–30
+  runs judged on the model's text stream; published rows judge transcripts of the audio.
+- **Logic puzzles**: 10 InteractionBench puzzles × 3 seeds, judged on the model's text stream.
+- **Spoken QA**: our rows are means over 3 seeds; Freeze-Omni, Moshi and Fun-Audio-Chat-Duplex-8B
+  are reported scores from Table 7 of the Fun-Audio-Chat technical report.
+
+Because our runs and the published results are scored differently, compare across systems with
+care. Full methodology is on the [project page](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html).
+
+## Inference Details
+
+<details>
+<summary>How the inference stack works</summary>
+
+Every 200 ms ("one frame") the model consumes
+
+* the user's audio of that frame, through a causal **ear** (a Whisper-style encoder with a
+  2 s attention window),
+* its own previous frame: one **text token** and five **codec tokens** of its own voice,
+
+and predicts the next text token and the next five codec tokens. The text lane carries the
+words and two control tokens: `pad` means "listening" and, during speech, "I am done" (the
+release); `sil` means "words done, audio still finishing". The codec tokens are rendered to
+speech by CosyVoice3's flow-matching decoder and HiFT vocoder.
+
+```
+mic 24 kHz ─► StreamingEar ─► 5 feature vectors per frame ─┐
+                                                           ▼
+   own text token ──► Qwen3-8B backbone (one position per frame) ──► next text token
+   own codec tokens ─┘                 │
+                                       ▼
+                          depth decoder (5 codec tokens / frame) ──► CosyVoice3 flow + HiFT ──► speaker
+```
+
+Both transformer stacks run through a fused, CUDA-graphed single-token decoder with static
+key/value caches (`awaken_chat/inference/static_decoder.py`), so a frame costs a few
+milliseconds of launch overhead instead of thousands of small kernels.
+
+</details>
+
+<details>
+<summary>Decode parameters</summary>
+
+`DecodeParams` (`awaken_chat/inference/params.py`) has two presets. `bench` is the recipe the
+reported numbers were produced with (text T 0.7 / top-k 25, audio T 0.8 / top-k 250).
+`live` is sharper inside a text burst (T 0.2 / top-k 5), penalizes repeated content tokens and
+caps consecutive near-silent codec tokens; it is what the server uses by default. Any field
+can be overridden per conversation in the `start` message.
+
+</details>
+
+<details>
+<summary>Repository layout</summary>
+
+```
+awaken_chat/
+  frame.py              frame rate, group size, sample rates, special token ids, system prompt
+  models/               configuration, ear, token embedder, depth decoder, AwakenModel, loaders
+  inference/            static decoder, session state machine, sampling, decode params, offline runner
+  codec/                CosyVoice3 flow + HiFT (vendored, trimmed), streaming detokenizer, worker process, voices
+  server/               WebSocket protocol, live session runner, FastAPI app
+  cli.py                awaken-server / awaken-offline / awaken-voice
+scripts/                ws_client.py, a paced WebSocket client that drives the server from a wav file
+tests/                  CPU unit tests on tiny random models
+hf_model_card/          Hugging Face model card (README + figures)
+webpage/                project page
+```
+
+</details>
+
+## License
+
+The code and model weights are licensed under the [Apache 2.0 License](https://github.com/OpenmartAI/awake-audio-chat/blob/main/LICENSE), the same license
+as the base model [Fun-Audio-Chat-8B](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B).
+
+## Acknowledgments
 
-## Contents and provenance
+The model architecture is derived from [Fun-Audio-Chat](https://github.com/FunAudioLLM/Fun-Audio-Chat)
+and the speech codec from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (both Apache-2.0);
+the text backbone is [Qwen3](https://github.com/QwenLM/Qwen3), and the ear follows
+[Whisper](https://github.com/openai/whisper)'s encoder. See [`NOTICE`](https://github.com/OpenmartAI/awake-audio-chat/blob/main/NOTICE).
 
-Eight safetensors shards, their index, the tokenizer files, and the inference config are
-included. Config normalization selects `AwakenModel` and the duplex `add` fusion mode,
-matching `load_model`. Source weights and tokenizer are unchanged. See `provenance.json`.
-Training arguments, trainer logs, credentials, codec binaries and TensorRT plans are excluded.
+## Contact
 
-The `grpo-160` checkpoint used for initial service validation is a separate checkpoint;
-its results do not establish the speech quality of this talker checkpoint.
+- 🐛 Submit an [Issue](https://github.com/OpenmartAI/awake-audio-chat/issues)
+- 💡 Submit a [Pull Request](https://github.com/OpenmartAI/awake-audio-chat/pulls)

SHA256SUMS

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