lighteternal
commited on
Commit
•
626bc84
1
Parent(s):
75ed774
Added new model weights and info, trained on CV and CSS
Browse filesThis view is limited to 50 files because it contains too many changes.
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- .ipynb_checkpoints/ASR_Inference-checkpoint.ipynb +498 -135
- ASR_Inference.ipynb +498 -135
- README.md +8 -6
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429242.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429243.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429245.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429246.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429247.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429253.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429254.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429255.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429256.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429257.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429268.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429269.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429270.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429271.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429272.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429278.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429280.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429283.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429285.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429288.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429298.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429299.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429300.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429301.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429302.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429308.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429309.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429310.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429312.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429314.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429328.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429329.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429330.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429331.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429332.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429407.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429408.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429410.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429411.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429412.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429418.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429419.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429420.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429421.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429422.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429438.mp3 +0 -0
- cv-corpus-6.1-2020-12-11/el/clips/common_voice_el_20429439.mp3 +0 -0
.ipynb_checkpoints/ASR_Inference-checkpoint.ipynb
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"Downloading and preparing dataset common_voice/el (download: 363.89 MiB, generated: 4.75 MiB, post-processed: Unknown size, total: 368.64 MiB) to /home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f...\n"
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"text": [
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"Dataset common_voice downloaded and prepared to /home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f. Subsequent calls will reuse this data.\n"
|
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|
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|
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+
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|
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]
|
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+
}
|
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+
],
|
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+
"source": [
|
845 |
+
"def map_to_result(batch):\n",
|
846 |
+
" model.to(\"cuda\")\n",
|
847 |
+
" input_values = processor(\n",
|
848 |
+
" batch[\"input_values\"], \n",
|
849 |
+
" sampling_rate=16_000, \n",
|
850 |
+
" return_tensors=\"pt\"\n",
|
851 |
+
" ).input_values.to(\"cuda\")\n",
|
852 |
+
"\n",
|
853 |
+
" with torch.no_grad():\n",
|
854 |
+
" logits = model(input_values).logits\n",
|
855 |
+
"\n",
|
856 |
+
" pred_ids = torch.argmax(logits, dim=-1)\n",
|
857 |
+
" batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n",
|
858 |
+
"\n",
|
859 |
+
" return batch\n",
|
860 |
+
"\n",
|
861 |
+
"results = common_voice_test.map(map_to_result)\n"
|
862 |
+
]
|
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+
},
|
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+
{
|
865 |
+
"cell_type": "code",
|
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+
"execution_count": 16,
|
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+
"metadata": {
|
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+
"ExecuteTime": {
|
869 |
+
"end_time": "2021-03-17T11:17:11.951524Z",
|
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+
"start_time": "2021-03-17T11:17:08.856552Z"
|
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+
}
|
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+
},
|
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+
"outputs": [
|
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+
{
|
875 |
+
"name": "stdout",
|
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+
"output_type": "stream",
|
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+
"text": [
|
878 |
+
"Test WER: 0.396\n"
|
879 |
+
]
|
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+
}
|
881 |
+
],
|
882 |
+
"source": [
|
883 |
+
"def compute_metrics(pred):\n",
|
884 |
+
" pred_logits = pred.predictions\n",
|
885 |
+
" pred_ids = np.argmax(pred_logits, axis=-1)\n",
|
886 |
+
"\n",
|
887 |
+
" pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
|
888 |
+
"\n",
|
889 |
+
" pred_str = processor.batch_decode(pred_ids)\n",
|
890 |
+
" # we do not want to group tokens when computing the metrics\n",
|
891 |
+
" label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
|
892 |
+
"\n",
|
893 |
+
" wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
|
894 |
+
"\n",
|
895 |
+
" return {\"wer\": wer}\n",
|
896 |
+
"\n",
|
897 |
+
"wer_metric = load_metric(\"wer\")\n",
|
898 |
+
"\n",
|
899 |
+
"print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references= [item.lower() for item in common_voice_test_transcription['sentence']])))"
|
900 |
+
]
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},
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{
|
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"cell_type": "code",
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"execution_count": null,
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ASR_Inference.ipynb
CHANGED
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"metadata": {
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"ExecuteTime": {
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@@ -36,8 +36,8 @@
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"outputs": [],
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@@ -110,8 +110,120 @@
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"output_type": "stream",
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"start_time": "2021-03-17T11:14:08.198478Z"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"name": "stdout",
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"text": [
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"Downloading and preparing dataset common_voice/el (download: 363.89 MiB, generated: 4.75 MiB, post-processed: Unknown size, total: 368.64 MiB) to /home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f...\n"
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"name": "stdout",
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"text": [
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"text": [
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"\r",
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"Dataset common_voice downloaded and prepared to /home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/0041e06ab061b91d0a23234a2221e87970a19cf3a81b20901474cffffeb7869f. Subsequent calls will reuse this data.\n"
|
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|
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}
|
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],
|
|
|
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"# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς."
|
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]
|
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{
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{
|
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"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
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"\n"
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]
|
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}
|
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],
|
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"source": [
|
845 |
+
"def map_to_result(batch):\n",
|
846 |
+
" model.to(\"cuda\")\n",
|
847 |
+
" input_values = processor(\n",
|
848 |
+
" batch[\"input_values\"], \n",
|
849 |
+
" sampling_rate=16_000, \n",
|
850 |
+
" return_tensors=\"pt\"\n",
|
851 |
+
" ).input_values.to(\"cuda\")\n",
|
852 |
+
"\n",
|
853 |
+
" with torch.no_grad():\n",
|
854 |
+
" logits = model(input_values).logits\n",
|
855 |
+
"\n",
|
856 |
+
" pred_ids = torch.argmax(logits, dim=-1)\n",
|
857 |
+
" batch[\"pred_str\"] = processor.batch_decode(pred_ids)[0]\n",
|
858 |
+
"\n",
|
859 |
+
" return batch\n",
|
860 |
+
"\n",
|
861 |
+
"results = common_voice_test.map(map_to_result)\n"
|
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+
]
|
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+
},
|
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{
|
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|
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"metadata": {
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|
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}
|
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},
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"outputs": [
|
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{
|
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+
"name": "stdout",
|
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"output_type": "stream",
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"text": [
|
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+
"Test WER: 0.396\n"
|
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]
|
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}
|
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],
|
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"source": [
|
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+
"def compute_metrics(pred):\n",
|
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+
" pred_logits = pred.predictions\n",
|
885 |
+
" pred_ids = np.argmax(pred_logits, axis=-1)\n",
|
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+
"\n",
|
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+
" pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
|
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+
"\n",
|
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+
" pred_str = processor.batch_decode(pred_ids)\n",
|
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+
" # we do not want to group tokens when computing the metrics\n",
|
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+
" label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
|
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+
"\n",
|
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" wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
|
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+
"\n",
|
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+
" return {\"wer\": wer}\n",
|
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"\n",
|
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+
"wer_metric = load_metric(\"wer\")\n",
|
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+
"\n",
|
899 |
+
"print(\"Test WER: {:.3f}\".format(wer_metric.compute(predictions=results[\"pred_str\"], references= [item.lower() for item in common_voice_test_transcription['sentence']])))"
|
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+
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
|
README.md
CHANGED
@@ -21,7 +21,7 @@ model-index:
|
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metrics:
|
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- name: Test WER
|
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type: wer
|
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-
value:
|
25 |
---
|
26 |
|
27 |
# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
|
@@ -29,12 +29,14 @@ model-index:
|
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* language: el
|
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* licence: apache-2.0
|
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-
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets
|
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-
* model: XLSR-Wav2Vec2, trained for
|
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* metrics: Word Error Rate (WER)
|
35 |
|
36 |
## Model description
|
37 |
|
|
|
|
|
38 |
Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, Facebook AI presented XLSR-Wav2Vec2. XLSR stands for cross-lingual speech representations and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.
|
39 |
|
40 |
Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.
|
@@ -189,7 +191,7 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
|
|
189 |
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
|
190 |
```
|
191 |
|
192 |
-
**Test Result**:
|
193 |
|
194 |
### How to use for training:
|
195 |
|
@@ -200,9 +202,9 @@ Instructions and code to replicate the process are provided in the Fine_Tune_XLS
|
|
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|
201 |
| Metric | Value |
|
202 |
| ----------- | ----------- |
|
203 |
-
| Training Loss | 0.
|
204 |
| Validation Loss | 0.6062 |
|
205 |
-
| WER on CommonVoice Test *|
|
206 |
* Reference transcripts were lower-cased and striped of punctuation and special characters.
|
207 |
|
208 |
Full metrics log here:
|
|
|
21 |
metrics:
|
22 |
- name: Test WER
|
23 |
type: wer
|
24 |
+
value: 10.497628
|
25 |
---
|
26 |
|
27 |
# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
|
|
|
29 |
|
30 |
* language: el
|
31 |
* licence: apache-2.0
|
32 |
+
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets + CSS10 (EL), 1.22GB: https://github.com/Kyubyong/css10
|
33 |
+
* model: XLSR-Wav2Vec2, trained for 50 epochs
|
34 |
* metrics: Word Error Rate (WER)
|
35 |
|
36 |
## Model description
|
37 |
|
38 |
+
UPDATE: We repeated the fine-tuning process using an additional 1.22GB dataset from CSS10.
|
39 |
+
|
40 |
Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, Facebook AI presented XLSR-Wav2Vec2. XLSR stands for cross-lingual speech representations and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.
|
41 |
|
42 |
Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.
|
|
|
191 |
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
|
192 |
```
|
193 |
|
194 |
+
**Test Result**: 10.497628 %
|
195 |
|
196 |
### How to use for training:
|
197 |
|
|
|
202 |
|
203 |
| Metric | Value |
|
204 |
| ----------- | ----------- |
|
205 |
+
| Training Loss | 0.0545 |
|
206 |
| Validation Loss | 0.6062 |
|
207 |
+
| WER on CommonVoice Test (%) *| 10.497628 |
|
208 |
* Reference transcripts were lower-cased and striped of punctuation and special characters.
|
209 |
|
210 |
Full metrics log here:
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