Finnish-NLP
/

wav2vec2-xlsr-300m-finnish-lm

Automatic Speech Recognition

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hf-asr-leaderboard

robust-speech-event

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aapot commited on May 16, 2022

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686734a

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1 Parent(s): deb1c3b

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@@ -191,10 +191,10 @@ Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggi
 To evaluate this model, run the `eval.py` script in this repository:
 ```bash
-python3 eval.py --model_id Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
 ```
-This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts:
 |                                                    | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
 |----------------------------------------------------|------------------|---------------|------------------|---------------|------------------|
@@ -208,10 +208,10 @@ This model (the third row of the table) achieves the following WER (Word Error R
 To evaluate this model, run the `eval.py` script in this repository:
 ```bash
-python3 eval.py --model_id Finnish-NLP/wav2vec2-base-fi-voxpopuli-v2-finetuned --dataset mozilla-foundation/common_voice_9_0 --config fi --split test
 ```
-This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts:
 |                                                    | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
 |----------------------------------------------------|------------------|---------------|------------------|---------------|------------------|

 To evaluate this model, run the `eval.py` script in this repository:
 ```bash
+python3 eval.py --model_id Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm  --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
 ```
+This model (the fourth row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts:
 |                                                    | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
 |----------------------------------------------------|------------------|---------------|------------------|---------------|------------------|
 To evaluate this model, run the `eval.py` script in this repository:
 ```bash
+python3 eval.py --model_id Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm  --dataset mozilla-foundation/common_voice_9_0 --config fi --split test
 ```
+This model (the fourth row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models and their parameter counts:
 |                                                    | Model parameters | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
 |----------------------------------------------------|------------------|---------------|------------------|---------------|------------------|