ai-sage/GigaAM-v3
automatic-speech-recognition model by ai-sage
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Model card & documentation
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GigaAM-v3 is a Conformer-based foundation model with 220–240M parameters, pretrained on diverse Russian speech data using the HuBERT-CTC objective. It is the third generation of the GigaAM family and provides state-of-the-art performance on Russian ASR across a wide range of domains. GigaAM-v3 includes the following model variants: GigaAM-v3 training incorporates new internal datasets: callcenter conversations, speech with background music, natural speech, and speech with atypical characteristics. the models perform on average 30% better on these new domains, while maintaining the same quality as previous GigaAM generations on public benchmarks. The table below reports the Word Error Rate (%) for GigaAM-v3 and other existing…
Read the full model card ↗ · Preview checked 2026-10-30T01:34:27.126Z
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Model overview
ai-sage/GigaAM-v3 is a automatic-speech-recognition model repository published by ai-sage on Hugging Face. A library was not reported.
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Model facts
- Task
- automatic-speech-recognition
- Library
- Not reported
- Recent downloads (30 days)
- 351,207
- Cumulative likes
- 195
- Hugging Face trending score
- 2
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2025-11-19T20:38:07.000Z
- Last modified
- 2025-11-19T21:45:34.000Z
Compatibility and lineage
Reported languages: ru, en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:32.899Z. Daily imports are snapshots, not real-time monitoring.
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