lucio
/

xls-r-uyghur-cv8

@@ -1,66 +1,38 @@
 ---
-language:
-- ug
 license: apache-2.0
 tags:
-- automatic-speech-recognition
-- mozilla-foundation/common_voice_8_0
 - generated_from_trainer
-- ug
-- robust-speech-event
 datasets:
-- mozilla-foundation/common_voice_8_0
 model-index:
-- name: XLS-R-300M Uyghur CV8
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 8
-      type: mozilla-foundation/common_voice_8_0
-      args: ug
-    metrics:
-       - name: Test WER
-         type: wer
-         value: 28.74
-       - name: Test CER
-         type: cer
-         value: 5.38
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
-# XLS-R-300M Uyghur CV8
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UG dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2036
-- WER: 0.2977
 ## Model description
-For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)
-The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed.
 ## Intended uses & limitations
-This model is expected to be of some utility for low-fidelity use cases such as:
-- Draft video captions
-- Indexing of recorded broadcasts
-The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers.
 ## Training and evaluation data
-The combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation.
 ## Training procedure
-The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs).
 ### Training hyperparameters
 The following hyperparameters were used during training:
@@ -80,48 +52,31 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss | Wer    |
 |:-------------:|:-----:|:-----:|:---------------:|:------:|
-| 3.2892        | 2.66  | 500   | 3.2415          | 1.0    |
-| 2.9206        | 5.32  | 1000  | 2.4381          | 1.0056 |
-| 1.4909        | 7.97  | 1500  | 0.5428          | 0.6705 |
-| 1.3395        | 10.64 | 2000  | 0.4207          | 0.5995 |
-| 1.2718        | 13.3  | 2500  | 0.3743          | 0.5648 |
-| 1.1798        | 15.95 | 3000  | 0.3225          | 0.4927 |
-| 1.1392        | 18.61 | 3500  | 0.3097          | 0.4627 |
-| 1.1143        | 21.28 | 4000  | 0.2996          | 0.4505 |
-| 1.0923        | 23.93 | 4500  | 0.2841          | 0.4229 |
-| 1.0516        | 26.59 | 5000  | 0.2705          | 0.4113 |
-| 1.051         | 29.25 | 5500  | 0.2622          | 0.4078 |
-| 1.021         | 31.91 | 6000  | 0.2611          | 0.4009 |
-| 0.9886        | 34.57 | 6500  | 0.2498          | 0.3921 |
-| 0.984         | 37.23 | 7000  | 0.2521          | 0.3845 |
-| 0.9631        | 39.89 | 7500  | 0.2413          | 0.3791 |
-| 0.9353        | 42.55 | 8000  | 0.2391          | 0.3612 |
-| 0.922         | 45.21 | 8500  | 0.2363          | 0.3571 |
-| 0.9116        | 47.87 | 9000  | 0.2285          | 0.3668 |
-| 0.8951        | 50.53 | 9500  | 0.2256          | 0.3729 |
-| 0.8865        | 53.19 | 10000 | 0.2228          | 0.3663 |
-| 0.8792        | 55.85 | 10500 | 0.2221          | 0.3656 |
-| 0.8682        | 58.51 | 11000 | 0.2228          | 0.3323 |
-| 0.8492        | 61.17 | 11500 | 0.2167          | 0.3446 |
-| 0.8365        | 63.83 | 12000 | 0.2156          | 0.3321 |
-| 0.8298        | 66.49 | 12500 | 0.2142          | 0.3400 |
-| 0.808         | 69.15 | 13000 | 0.2079          | 0.3148 |
-| 0.7999        | 71.81 | 13500 | 0.2117          | 0.3225 |
-| 0.7871        | 74.47 | 14000 | 0.2088          | 0.3174 |
-| 0.7858        | 77.13 | 14500 | 0.2060          | 0.3008 |
-| 0.7764        | 79.78 | 15000 | 0.2128          | 0.3146 |
-| 0.7684        | 82.45 | 15500 | 0.2086          | 0.3101 |
-| 0.7717        | 85.11 | 16000 | 0.2048          | 0.3069 |
-| 0.7435        | 87.76 | 16500 | 0.2027          | 0.3055 |
-| 0.7378        | 90.42 | 17000 | 0.2059          | 0.2993 |
-| 0.7406        | 93.08 | 17500 | 0.2040          | 0.2966 |
-| 0.7361        | 95.74 | 18000 | 0.2056          | 0.3000 |
-| 0.7379        | 98.4  | 18500 | 0.2031          | 0.2976 |
 ### Framework versions
-- Transformers 4.16.0.dev0
-- Pytorch 1.10.1+cu102
-- Datasets 1.18.2.dev0
 - Tokenizers 0.11.0

 ---
 license: apache-2.0
 tags:
 - generated_from_trainer
 datasets:
+- common_voice
 model-index:
+- name: xls-r-uyghur-cv8
+  results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
+# xls-r-uyghur-cv8
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.2163
+- Wer: 0.3241
 ## Model description
+More information needed
 ## Intended uses & limitations
+More information needed
 ## Training and evaluation data
+More information needed
 ## Training procedure
 ### Training hyperparameters
 The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss | Wer    |
 |:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 3.2914        | 4.85  | 500   | 3.2283          | 1.0    |
+| 3.0068        | 9.71  | 1000  | 2.7939          | 0.9980 |
+| 1.4306        | 14.56 | 1500  | 0.4857          | 0.6314 |
+| 1.2831        | 19.42 | 2000  | 0.3679          | 0.6066 |
+| 1.2065        | 24.27 | 2500  | 0.3303          | 0.5560 |
+| 1.1449        | 29.13 | 3000  | 0.3008          | 0.4690 |
+| 1.0926        | 33.98 | 3500  | 0.2817          | 0.4619 |
+| 1.0635        | 38.83 | 4000  | 0.2665          | 0.4391 |
+| 1.029         | 43.69 | 4500  | 0.2616          | 0.4175 |
+| 1.0064        | 48.54 | 5000  | 0.2468          | 0.4051 |
+| 0.9659        | 53.4  | 5500  | 0.2394          | 0.3860 |
+| 0.9254        | 58.25 | 6000  | 0.2373          | 0.3689 |
+| 0.9209        | 63.11 | 6500  | 0.2347          | 0.3670 |
+| 0.889         | 67.96 | 7000  | 0.2291          | 0.3687 |
+| 0.8859        | 72.82 | 7500  | 0.2272          | 0.3616 |
+| 0.8441        | 77.67 | 8000  | 0.2232          | 0.3538 |
+| 0.8284        | 82.52 | 8500  | 0.2224          | 0.3382 |
+| 0.8142        | 87.38 | 9000  | 0.2193          | 0.3310 |
+| 0.8012        | 92.23 | 9500  | 0.2168          | 0.3276 |
+| 0.7781        | 97.09 | 10000 | 0.2163          | 0.3241 |
 ### Framework versions
+- Transformers 4.17.0.dev0
+- Pytorch 1.10.2+cu102
+- Datasets 1.18.3
 - Tokenizers 0.11.0