Dubedo
/

VibeVoice-ASR-INT8

@@ -1,199 +1,137 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: mit
+base_model: microsoft/VibeVoice-ASR
+tags:
+  - automatic-speech-recognition
+  - vibevoice
+  - bitsandbytes
+  - 8-bit
+  - int8
+  - quantized
+  - diarization
+  - multilingual
+pipeline_tag: automatic-speech-recognition
 library_name: transformers
 ---
+# VibeVoice-ASR — Selective INT8 Quantization
+Selectively quantized version of [microsoft/VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR) for low-VRAM deployment.
+**Only the Qwen2.5-7B LLM backbone is quantized to INT8.** Audio tokenizers, connectors, and lm_head remain in full BF16 precision — preserving diarization accuracy and transcription quality.
+> ⚠️ This model uses the **standalone** `vibevoice` package (`pip install git+https://github.com/microsoft/VibeVoice.git`), NOT the HF-native `transformers >= 5.3.0` variant. It requires `transformers == 4.57.3`.
+## Key details
+| | |
+|---|---|
+| Base model | [microsoft/VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR) |
+| Quantization | INT8 (bitsandbytes `Linear8bitLt`) |
+| Modules quantized | `model.language_model.model.layers.*` (196 layers) |
+| Modules in BF16 | `acoustic_tokenizer`, `semantic_tokenizer`, `acoustic_connector`, `semantic_connector`, `lm_head` (161 layers) |
+| Model size | ~9.2 GB (down from 17.3 GB) |
+| Peak VRAM | ~12.5 GB (including inference activations) |
+| Transformers | == 4.57.3 |
+| bitsandbytes | >= 0.48.1 |
+## Why selective quantization?
+Naive INT8 quantization of the entire model produces `[Unintelligible Speech]` — the model detects speech boundaries but cannot decode content. The acoustic and semantic tokenizer encoders process raw audio signals where quantization errors propagate catastrophically. The LLM backbone (Qwen2.5-7B) handles INT8 quantization gracefully.
+**Critical discovery:** The standalone `vibevoice` package uses different module names than the HF-native variant. The correct skip list for the standalone model is:
+| Standalone (this model) | HF-native (won't work here) |
+|---|---|
+| `acoustic_tokenizer` | `acoustic_tokenizer_encoder` |
+| `semantic_tokenizer` | `semantic_tokenizer_encoder` |
+| `acoustic_connector` | `acoustic_projection` |
+| `semantic_connector` | `semantic_projection` |
+Using the HF-native names with the standalone package silently quantizes audio-critical modules, producing garbage output.
+## Usage
+```python
+import torch
+from vibevoice.modular.modeling_vibevoice_asr import VibeVoiceASRForConditionalGeneration
+from vibevoice.processor.vibevoice_asr_processor import VibeVoiceASRProcessor
+model_id = "Dubedo/VibeVoice-ASR-INT8"
+# Load processor (no preprocessor_config.json — default ratio=3200 is correct)
+processor = VibeVoiceASRProcessor.from_pretrained(
+    model_id,
+    language_model_pretrained_name="Qwen/Qwen2.5-7B",
+)
+# Load quantized model
+model = VibeVoiceASRForConditionalGeneration.from_pretrained(
+    model_id,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.eval()
+# Transcribe
+inputs = processor(
+    audio=["path/to/audio.wav"],
+    sampling_rate=None,
+    return_tensors="pt",
+    padding=True,
+    add_generation_prompt=True,
+)
+inputs = {k: v.to("cuda") if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=32768,
+        pad_token_id=processor.pad_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        do_sample=False,
+    )
+input_length = inputs["input_ids"].shape[1]
+generated_ids = output_ids[0, input_length:]
+text = processor.decode(generated_ids, skip_special_tokens=True)
+segments = processor.post_process_transcription(text)
+```
+## Quantization method
+Quantized on NVIDIA L4 (22GB) using the standalone `vibevoice` package with `BitsAndBytesConfig`:
+```python
+from transformers import BitsAndBytesConfig
+quantization_config = BitsAndBytesConfig(
+    load_in_8bit=True,
+    llm_int8_skip_modules=[
+        "acoustic_tokenizer",
+        "semantic_tokenizer",
+        "acoustic_connector",
+        "semantic_connector",
+        "lm_head",
+    ],
+)
+model = VibeVoiceASRForConditionalGeneration.from_pretrained(
+    "microsoft/VibeVoice-ASR",
+    quantization_config=quantization_config,
+    torch_dtype=torch.bfloat16,
+    dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+```
+## Important notes
+- **Do NOT create a `preprocessor_config.json`** — the standalone processor's default fallback sets `speech_tok_compress_ratio=3200`, which is correct. Creating one with `ratio=320` causes a 10x mask shape mismatch and `IndexError`.
+- **Requires `bitsandbytes >= 0.48.1`** — v0.48.0 has a confirmed critical bug breaking INT8 quantization.
+- **INT8 models cannot be moved between CPU and GPU** — use delete+reload pattern for VRAM management.
+## Acknowledgments
+Based on [microsoft/VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR). Built for the [Dubedo](https://dubedo.com) AI video dubbing platform.