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Avista Large Plus model by enactic
â 37.6
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Model
Avista Large Plus
by enactic hf-model--enactic--avista-large-plus
Free2AITools Nexus Index
37.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 3
R: Recency 97
Q: Quality 50
Tech Context
Vital Performance 0.0%
31 DL / 30D
Audited 37.6 FNI Score
Tiny - Params
- Context
31 Downloads
Dense AVHUBERTFORCONDITIONALGENERATION Architecture
Commercial APACHE License
| Entity Passport | |
| Registry ID | hf-model--enactic--avista-large-plus |
| License | Apache-2.0 |
| Provider | huggingface |
đ
Cite this model
Academic & Research Attribution
BibTeX
@misc{hf_model__enactic__avista_large_plus,
author = {enactic},
title = {Avista Large Plus Model},
year = {2026},
howpublished = {\url{https://huggingface.co/enactic/avista-large-plus}},
note = {Accessed via Free2AITools Knowledge Fortress}
} APA Style
enactic. (2026). Avista Large Plus [Model]. Free2AITools. https://huggingface.co/enactic/avista-large-plus
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Quick Commands
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huggingface-cli download enactic/avista-large-plus đĻ Install Lib
pip install -U transformers âī¸ Free2AITools Nexus Index V2.0
Semantic (S) 50
Authority (A) 0
Popularity (P) 3
Recency (R) 97
Quality (Q) 50
đŦ Index Insight
FNI V2.0 for Avista Large Plus: Semantic (S:50), Authority (A:0), Popularity (P:3), Recency (R:97), Quality (Q:50).
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AVista Large+ đĻâđĨ
This is AVHuBERT (Audio-Visual Hidden Unit BERT) Large model for AVSR (Audio-Visual Speech Recognition) task, derived from enactic/japanese-avhubert-large_noise_pt.
This model is fine-tuned on approximately 1,300h of Japanese audio-visual dataset.
Usage
Please install dependencies first.
bash
$ pip install git+https://github.com/reazon-research/ReazonSpeech.git#subdirectory=pkg/avsrUsing `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-large-plus", trust_remote_code=True)
model = AutoModelForSpeechSeq2Seq.from_pretrained("enactic/avista-large-plus", 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-large-plus")
model = AVHubertForConditionalGeneration.from_pretrained("enactic/avista-large-plus")
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.
+ ReazonSpeech Speech
| Model | #Params | N/A | SNR=10 | SNR=5 | SNR=0 | SNR=-5 |
|---|---|---|---|---|---|---|
| AVista Large+ | 459M | 21.57% | 27.73% | 31.36% | 39.61% | 54.82% |
| 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 Large+ | 459M | 21.57% | 26.26% | 28.21% | 30.92% | 36.89% |
| 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 Large+ | 459M | 21.57% | 26.85% | 33.91% | 51.24% | 79.48% |
| 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 Large+ | 459M | 21.57% | 24.42% | 27.63% | 34.68% | 49.39% |
| 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 Large+ | 459M | 21.57% | 23.57% | 26.04% | 31.29% | 38.91% |
| 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
text
@misc{enactic/avista-large-plus,
title={avista-large-plus},
author={Sasaki, Yuta},
url = {https://huggingface.co/enactic/avista-large-plus},
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
â ī¸ Incomplete Data
Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.
View Original Source âđ Limitations & Considerations
- âĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
- âĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
- âĸ FNI scores are relative rankings and may change as new models are added.
- â License Unknown: Verify licensing terms before commercial use.
Social Proof
HuggingFace Hub
31Downloads
đ Daily sync (03:00 UTC)
AI Summary: Based on Hugging Face metadata. Not a recommendation.
đĄī¸ Model Transparency Report
Technical metadata sourced from upstream repositories.
Open Metadata
đ Identity & Source
- id
- hf-model--enactic--avista-large-plus
- slug
- enactic--avista-large-plus
- source
- huggingface
- author
- enactic
- license
- Apache-2.0
- tags
- transformers, pytorch, avhubert, automatic-speech-recognition, avsr, custom_code, ja, arxiv:2201.02184, arxiv:2201.01763, license:apache-2.0, region:us
âī¸ Technical Specs
- architecture
- AVHubertForConditionalGeneration
- params billions
- null
- context length
- null
- pipeline tag
- automatic-speech-recognition
đ Engagement & Metrics
- downloads
- 31
- stars
- 0
- forks
- 0
Data indexed from public sources. Updated daily.