enactic
/

avista-base-plus-v2

@@ -1,199 +1,161 @@
 ---
 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]

 ---
 library_name: transformers
+tags:
+- AVSR
+- AVHuBERT
+language:
+- ja
+pipeline_tag: automatic-speech-recognition
+base_model:
+- enactic/japanese-avhubert-base_noise_pt
+metrics:
+- cer
+license: apache-2.0
 ---
+# AVista Base+ v2 🐦‍🔥
+This is AVHuBERT (Audio-Visual Hidden Unit BERT) Base model for AVSR (Audio-Visual Speech Recognition) task, derived from [`enactic/japanese-avhubert-base_noise_pt`](https://huggingface.co/enactic/japanese-avhubert-base_noise_pt).
+This model is fine-tuned on approximately 1,300h of Japanese audio-visual dataset combined with 35,000h of Japanese audio-only dataset, [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech).
+## Usage
+Please install dependencies first.
+```bash
+$ pip install git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/avsr
+```
+### Using `transformers` directly
+You can load AVSR models by directly using Hugging Face transformers if you trust our remote code.
+```python
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
+processor = AutoProcessor.from_pretrained("enactic/avista-base-plus-v2", trust_remote_code=True)
+model = AutoModelForSpeechSeq2Seq.from_pretrained("enactic/avista-base-plus-v2", trust_remote_code=True)
+inputs = processor(raw_audio="path/to/audio", raw_video="path/to/video")
+# If mouth extraction is not performed, you can add `extract_mouth=True`
+inputs = processor(raw_audio="path/to/audio", raw_video="path/to/video", extract_mouth=True)
+outputs = model.generate(**inputs, num_beams=5, max_new_tokens=256)
+transcription = processor.decode(outputs[0], skip_special_tokens=True)
+```
+### Using `reazonspeech.avsr` package
+You can also load AVSR models by using reazonspeech.avsr. If you don't want to use remote code for security reasons for example, you can use the following code.
+```python
+from reazonspeech.avsr import AVHubertProcessor, AVHubertForConditionalGeneration
+processor = AVHubertProcessor.from_pretrained("enactic/avista-base-plus-v2")
+model = AVHubertForConditionalGeneration.from_pretrained("enactic/avista-base-plus-v2")
+inputs = processor(raw_audio="path/to/audio", raw_video="path/to/video")
+# If mouth extraction is not performed, you can add `extract_mouth=True`
+inputs = processor(raw_audio="path/to/audio", raw_video="path/to/video", extract_mouth=True)
+outputs = model.generate(**inputs, num_beams=5, max_new_tokens=256)
+transcription = processor.decode(outputs[0], skip_special_tokens=True)
+```
+## Test Results
+We report the Character Error Rate (CER) on an out-of-domain evaluation dataset that was internally collected for AVSR benchmarking.
+The following table presents the benchmark results of this model and Japanese ASR models under different noise levels and noise types.
+Details of the dataset and the complete benchmark results can be found [here](https://huggingface.co/datasets/enactic/avsr-leaderboard).
+**+ ReazonSpeech Speech**
+| Model               | #Params |  N/A   | SNR=10 |  SNR=5 |   SNR=0 |  SNR=-5 |
+| :------------------ | ------: | -----: | -----: | -----: | ------: | ------: |
+| AVista Base+ v2     |   156M | 10.77% | 15.73% | 23.08% |  62.02% |  98.19% |
+| reazonspeech k2     |   159M |  7.42% |  9.13% | 19.47% |  71.61% | 104.15% |
+| reazonspeech nemo   |   619M |  8.50% | 11.74% | 25.38% |  77.65% | 103.42% |
+| reazonspeech espnet |   118M |  7.44% |  9.20% | 16.58% |  69.34% | 103.22% |
+| whisper large-v3    | 1,550M |  7.75% |  8.70% | 12.81% |  49.34% | 100.53% |
+| whisper medium      |   769M | 10.07% | 13.23% | 19.21% |  50.56% |  99.27% |
+| whisper small       |   244M | 10.82% | 19.82% | 28.98% |  69.69% | 108.56% |
+**+ JSUT Speech**
+| Model               | #Params |  N/A   | SNR=10 |  SNR=5 |   SNR=0 |  SNR=-5 |
+| :------------------ | ------: | -----: | -----: | -----: | ------: | ------: |
+| AVista Base+ v2     |   156M | 10.77% | 14.49% | 21.41% |  57.51% |  91.28% |
+| reazonspeech k2     |   159M |  7.42% |  8.49% | 21.94% |  70.81% |  93.04% |
+| reazonspeech nemo   |   619M |  8.50% | 10.93% | 29.06% |  83.77% |  98.76% |
+| reazonspeech espnet |   118M |  7.44% |  8.30% | 14.45% |  66.15% |  69.34% |
+| whisper large-v3    | 1,550M |  7.75% |  8.69% | 13.03% |  60.24% |  98.67% |
+| whisper medium      |   769M | 10.07% | 12.27% | 18.80% |  58.00% |  97.35% |
+| whisper small       |   244M | 10.82% | 19.44% | 26.75% |  71.33% | 101.84% |
+**+ Babble**
+| Model               | #Params |  N/A   | SNR=10 |  SNR=5 |   SNR=0 |  SNR=-5 |
+| :------------------ | ------: | -----: | -----: | -----: | ------: | ------: |
+| AVista Base+ v2     |   156M | 10.77% | 13.86% | 20.38% |  43.51% |  85.12% |
+| reazonspeech k2     |   159M |  7.42% |  8.24% | 10.17% |  21.65% |  61.57% |
+| reazonspeech nemo   |   619M |  8.50% | 10.40% | 14.83% |  31.74% |  77.29% |
+| reazonspeech espnet |   118M |  7.44% |  8.85% | 11.75% |  24.59% |  67.27% |
+| whisper large-v3    | 1,550M |  7.75% |  8.95% | 12.50% |  30.09% |  81.60% |
+| whisper medium      |   769M | 10.07% | 12.52% | 18.18% |  42.27% |  95.43% |
+| whisper small       |   244M | 10.82% | 19.72% | 28.24% |  56.72% | 109.61% |
+**+ Music**
+| Model               | #Params |  N/A   | SNR=10 |  SNR=5 |  SNR=0 |  SNR=-5 |
+| :------------------ | ------: | -----: | -----: | -----: | -----: | ------: |
+| AVista Base+ v2     |   156M | 10.77% | 12.51% | 14.81% | 21.85% |  40.73% |
+| reazonspeech k2     |   159M |  7.42% |  7.69% |  8.33% |  9.49% |  16.90% |
+| reazonspeech nemo   |   619M |  8.50% |  9.28% |  9.97% | 13.65% |  24.61% |
+| reazonspeech espnet |   118M |  7.44% |  7.86% |  8.57% | 10.41% |  16.62% |
+| whisper large-v3    | 1,550M |  7.75% |  8.16% |  9.01% | 11.23% |  21.26% |
+| whisper medium      |   769M | 10.07% | 11.13% | 12.97% | 16.45% |  31.62% |
+| whisper small       |   244M | 10.82% | 18.02% | 19.86% | 26.82% |  47.69% |
+**+ Environmental Noise**
+| Model               | #Params |  N/A   | SNR=10 |  SNR=5 |  SNR=0 | SNR=-5 |
+| :------------------ | ------: | -----: | -----: | -----: | -----: | -----: |
+| AVista Base+ v2     |   156M | 10.77% | 12.70% | 14.99% | 21.10% | 31.99% |
+| reazonspeech k2     |   159M |  7.42% |  8.07% |  8.68% | 10.32% | 15.53% |
+| reazonspeech nemo   |   619M |  8.50% |  9.31% | 10.16% | 12.71% | 18.32% |
+| reazonspeech espnet |   118M |  7.44% |  8.00% |  8.63% | 10.06% | 14.54% |
+| whisper large-v3    | 1,550M |  7.75% |  8.46% |  9.17% | 11.98% | 19.36% |
+| whisper medium      |   769M | 10.07% | 11.77% | 13.06% | 17.04% | 24.83% |
+| whisper small       |   244M | 10.82% | 17.62% | 19.84% | 25.55% | 33.77% |
+## Citation
+```
+@misc{enactic/avista-base-plus-v2,
+  title={avista-base-plus-v2},
+  author={Sasaki, Yuta},
+  url = {https://huggingface.co/enactic/avista-base-plus-v2},
+  year = {2025}
+}
+@article{shi2022avhubert,
+    author  = {Bowen Shi and Wei-Ning Hsu and Kushal Lakhotia and Abdelrahman Mohamed},
+    title = {Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction},
+    journal = {arXiv preprint arXiv:2201.02184}
+    year = {2022}
+}
+@article{shi2022avsr,
+    author  = {Bowen Shi and Wei-Ning Hsu and Abdelrahman Mohamed},
+    title = {Robust Self-Supervised Audio-Visual Speech Recognition},
+    journal = {arXiv preprint arXiv:2201.01763}
+    year = {2022}
+}
+```
+## License
+[Apache License 2.0](https://choosealicense.com/licenses/apache-2.0/)