Automatic Speech Recognition
NeMo
ONNX
GGUF
Danish
speech-recognition
speech-to-text
danish
danish-asr
offline
local-inference
cpu-inference
sherpa-onnx
rnnt
word-timestamps
Eval Results (legacy)
Instructions to use RyeAI/ekko-v1-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use RyeAI/ekko-v1-tiny with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("RyeAI/ekko-v1-tiny") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Correct Common Voice benchmark naming
Browse files
README.md
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@@ -57,8 +57,8 @@ model-index:
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- task:
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type: automatic-speech-recognition
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dataset:
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name: Common Voice
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type: mozilla-foundation/
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config: da
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split: test
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metrics:
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| CoRal conversation | Spontaneous conversation | 24.15% |
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| CoRal read aloud | Read speech | 11.87% |
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| Common Voice
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| FLEURS `da_dk` | Read news-style speech | 9.75% |
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| FTSpeech | Parliamentary speech | 7.62% |
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| **Equal-weight mean** | | **12.37%** |
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- task:
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type: automatic-speech-recognition
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dataset:
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name: Common Voice Danish (CV 25.0 test subset)
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type: mozilla-foundation/common_voice_27_0
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config: da
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split: test
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metrics:
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|---|---|---:|
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| CoRal conversation | Spontaneous conversation | 24.15% |
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| CoRal read aloud | Read speech | 11.87% |
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| Common Voice Danish (CV 25.0 test subset) | Crowd-sourced read speech | 8.44% |
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| FLEURS `da_dk` | Read news-style speech | 9.75% |
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| 120 |
| FTSpeech | Parliamentary speech | 7.62% |
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| 121 |
| **Equal-weight mean** | | **12.37%** |
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