update model

README.md

@@ -2,6 +2,8 @@
 language: en
 datasets:
 - common_voice
+- librispeech_asr
+- timit_asr
 metrics:
 - wer
 - cer
@@ -24,15 +26,15 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 19.18
+         value: 19.76
        - name: Test CER
          type: cer
-         value: 8.25
+         value: 8.60
 ---
 
 # Wav2Vec2-Large-XLSR-53-English
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice), [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) and [TIMIT](https://huggingface.co/datasets/timit_asr),.
 When using this model, make sure that your speech input is sampled at 16kHz.
 
 The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
@@ -81,16 +83,16 @@ for i, predicted_sentence in enumerate(predicted_sentences):
 
 | Reference  | Prediction |
 | ------------- | ------------- |
-| "SHE'LL BE ALL RIGHT." | SHE'LD BE ALL RIGHT |
+| "SHE'LL BE ALL RIGHT." | SHE'D BE ALRIGHT |
 | SIX | SIX |
 | "ALL'S WELL THAT ENDS WELL." | ALL IS WELL THAT ENDS WELL |
 | DO YOU MEAN IT? | DO YOU MEAN IT |
 | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESSION |
-| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOWIS MOCILE ARE GOING TO HANDLE AMBIGUITIES LIKE KU AND KU |
-| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RISSHON WAS INCAN IN THE BAK TE |
+| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MUSILA GOING TO HANDLE ANB HOOTIES LIKE QU AND QU |
+| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RISIONAS INCI IN TE BACTY |
 | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
-| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUISE IS SAUCED FOR THE GONDER |
-| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
+| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUISE IS SAUCE FOR THE GONDER |
+| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SOUNDS WHEN SHE WAS FOUR YEARS OLD |
 
 ## Evaluation
 
@@ -164,7 +166,7 @@ print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_
 
 **Test Result**:
 
-In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-20). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
+In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-20). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used... I've also tested the model using the LibriSpeech and TIMIT datasets, which are better-behaved datasets than the Common Voice, containing only examples in US English extracted from audiobooks.
 
 ---
 
@@ -172,7 +174,7 @@ In the table below I report the Word Error Rate (WER) and the Character Error Ra
 
 | Model | WER | CER |
 | ------------- | ------------- | ------------- |
-| jonatasgrosman/wav2vec2-large-xlsr-53-english | **19.18%** | **8.25%** |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | **19.76%** | **8.60%** |
 | jonatasgrosman/wav2vec2-large-english | 21.16% | 9.53% |
 | facebook/wav2vec2-large-960h-lv60-self | 22.03% | 10.39% |
 | facebook/wav2vec2-large-960h-lv60 | 23.97% | 11.14% |
@@ -194,8 +196,8 @@ In the table below I report the Word Error Rate (WER) and the Character Error Ra
 | facebook/wav2vec2-large-960h-lv60 | 2.15% | 0.61% |
 | facebook/wav2vec2-large-960h | 2.82% | 0.84% |
 | facebook/wav2vec2-base-960h | 3.44% | 1.06% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 4.16% | 1.28% |
 | facebook/wav2vec2-base-100h | 6.26% | 2.00% |
-| jonatasgrosman/wav2vec2-large-xlsr-53-english | 6.97% | 2.02% |
 | jonatasgrosman/wav2vec2-large-english | 8.00% | 2.55% |
 | elgeish/wav2vec2-large-lv60-timit-asr | 15.53% | 4.93% |
 | boris/xlsr-en-punctuation | 19.28% | 6.45% |
@@ -211,8 +213,8 @@ In the table below I report the Word Error Rate (WER) and the Character Error Ra
 | facebook/wav2vec2-large-960h-lv60-self | **3.89%** | **1.40%** |
 | facebook/wav2vec2-large-960h-lv60 | 4.45% | 1.56% |
 | facebook/wav2vec2-large-960h | 6.49% | 2.52% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 8.82% | 3.42% |
 | facebook/wav2vec2-base-960h | 8.90% | 3.55% |
-| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.75% | 4.23% |
 | jonatasgrosman/wav2vec2-large-english | 13.62% | 5.24% |
 | facebook/wav2vec2-base-100h | 13.97% | 5.51% |
 | boris/xlsr-en-punctuation | 26.40% | 10.11% |
@@ -228,9 +230,9 @@ In the table below I report the Word Error Rate (WER) and the Character Error Ra
 | ------------- | ------------- | ------------- |
 | facebook/wav2vec2-large-960h-lv60-self | **5.17%** | **1.33%** |
 | facebook/wav2vec2-large-960h-lv60 | 6.24% | 1.54% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 6.81% | 2.02% |
 | facebook/wav2vec2-large-960h | 9.63% | 2.19% |
 | facebook/wav2vec2-base-960h | 11.48% | 2.76% |
-| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.93% | 3.50% |
 | elgeish/wav2vec2-large-lv60-timit-asr | 13.83% | 4.36% |
 | jonatasgrosman/wav2vec2-large-english | 13.91% | 4.01% |
 | facebook/wav2vec2-base-100h | 16.75% | 4.79% |

pytorch_model.bin

@@ -1,3 +1,3 @@
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 size 1262069143