huseinzol05
commited on
Merge branch 'main' of https://huggingface.co/mesolitica/wav2vec2-xls-r-300m-mixed into main
Browse files- README.md +16 -16
- evaluate-gpu.ipynb +63 -151
README.md
CHANGED
@@ -35,37 +35,37 @@ It achieves the following results on the evaluation set based on [evaluate-wav2v
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Mixed evaluation,
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```
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CER: 0.
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WER: 0.
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CER with LM: 0.
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WER with LM: 0.
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```
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Malay evaluation,
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```
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CER: 0.
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WER: 0.
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CER with LM: 0.
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WER with LM: 0.
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```
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Singlish evaluation,
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```
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CER: 0.
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WER: 0.
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CER with LM: 0.
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WER with LM: 0.
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```
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Mandarin evaluation,
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```
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CER: 0.
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WER: 0.
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CER with LM: 0.
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WER with LM: 0.
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```
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Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined
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Mixed evaluation,
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```
|
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CER: 0.0481054244857041
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WER: 0.1322198446007387
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CER with LM: 0.041196586938584696
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WER with LM: 0.09880169127621556
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```
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Malay evaluation,
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```
|
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CER: 0.051636391937588406
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WER: 0.19561999547293663
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CER with LM: 0.03917689630621449
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WER with LM: 0.12710746406824835
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```
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Singlish evaluation,
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```
|
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CER: 0.0494915200071987
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WER: 0.12763802881676573
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CER with LM: 0.04271234986432335
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WER with LM: 0.09677160640413336
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```
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Mandarin evaluation,
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```
|
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CER: 0.035626554824269824
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WER: 0.07993515937860181
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CER with LM: 0.03487760945087219
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WER with LM: 0.07536807168546154
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```
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Language model from https://huggingface.co/huseinzol05/language-model-bahasa-manglish-combined
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evaluate-gpu.ipynb
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"\n",
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"TFWav2Vec2ForCTC has backpropagation operations that are NOT supported on CPU. If you wish to train/fine-tine this model, you need a GPU or a TPU\n",
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"2022-06-01 19:14:22.857691: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8100\n",
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"If your task is similar to the task the model of the checkpoint was trained on, you can already use TFWav2Vec2ForCTC for predictions without further training.\n"
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"[('singlish-test/3057.wav', 'the teenagers paddled hard on their boat'),\n",
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" 'a letter by a mans daughter pleading for leniency was submitted'),\n",
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" ('singlish-test/659.wav', 'and theres thousands of people to meet'),\n",
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" ('singlish-test/809.wav', 'how much lower are the prices'),\n",
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" 'suddenly a gun shot was fired from a distance which sent the dogs fleeing in an instant'),\n",
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" 'a stronger dollar pressures gold making it more expensive for holders of other currencies'),\n",
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" 'adakah anda percaya bahawa donald trump adalah kedatangan kedua jesus christ'),\n",
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"execution_count": 8,
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"model = AutoModelForCTC.from_pretrained(\n",
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"['the teenagers paddled hard on their boat',\n",
|
307 |
+
" 'kenapa justin tradio seperti kemaluan wanita',\n",
|
308 |
+
" 'a letter bya mans daughter pleading for lenien te was submitted',\n",
|
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+
" 'and theres thousands of people to meet']"
|
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]
|
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},
|
312 |
"execution_count": 14,
|
|
|
362 |
"name": "stdout",
|
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"output_type": "stream",
|
364 |
"text": [
|
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+
"0 to know more about this years budget click here\n",
|
366 |
+
"1 you can bake shortbread cookies just with sugar butter and flour\n",
|
367 |
+
"2 all good citizens should learn how to change a light bulb\n",
|
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+
"3 as a child madam surley was constantly teased by other children over her appearance\n"
|
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]
|
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}
|
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],
|
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{
|
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"data": {
|
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"text/plain": [
|
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+
"['to know more about this years budget click here',\n",
|
389 |
+
" 'you can bake shortbread cookies just with sugar butter and flour',\n",
|
390 |
+
" 'all good citizens should learn how to change a light bulb',\n",
|
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+
" 'as a child madam shirley was constantly teased by other children over her appearance']"
|
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]
|
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},
|
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"execution_count": 18,
|
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"name": "stderr",
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"output_type": "stream",
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"text": [
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+
"100%|█████��████| 1240/1240 [04:23<00:00, 4.71it/s]\n"
|
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]
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}
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],
|
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" cer.append(calculate_cer(batch_y[k], pred[k]))\n",
|
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" \n",
|
476 |
" wer_lm.append(calculate_wer(batch_y[k], d_lm2))\n",
|
477 |
+
" cer_lm.append(calculate_cer(batch_y[k], d_lm2))"
|
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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": 21,
|
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"id": "6c6ce8ef",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
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488 |
"text/plain": [
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+
"(0.1322198446007387,\n",
|
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+
" 0.0481054244857041,\n",
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+
" 0.09880169127621556,\n",
|
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+
" 0.041196586938584696)"
|
493 |
]
|
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},
|
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+
"execution_count": 21,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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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": 22,
|
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"id": "cf53914e",
|
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"metadata": {},
|
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"outputs": [],
|
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},
|
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{
|
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"cell_type": "code",
|
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+
"execution_count": 23,
|
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"id": "b1558987",
|
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"metadata": {},
|
521 |
"outputs": [
|
522 |
{
|
523 |
"data": {
|
524 |
"text/plain": [
|
525 |
+
"(0.19561999547293663,\n",
|
526 |
+
" 0.051636391937588406,\n",
|
527 |
+
" 0.12710746406824835,\n",
|
528 |
+
" 0.03917689630621449)"
|
529 |
]
|
530 |
},
|
531 |
+
"execution_count": 23,
|
532 |
"metadata": {},
|
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"output_type": "execute_result"
|
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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": 24,
|
543 |
"id": "f340cde7",
|
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"metadata": {},
|
545 |
"outputs": [
|
546 |
{
|
547 |
"data": {
|
548 |
"text/plain": [
|
549 |
+
"(0.12763802881676573,\n",
|
550 |
+
" 0.0494915200071987,\n",
|
551 |
+
" 0.09677160640413336,\n",
|
552 |
+
" 0.04271234986432335)"
|
553 |
]
|
554 |
},
|
555 |
+
"execution_count": 24,
|
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"metadata": {},
|
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"output_type": "execute_result"
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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": 26,
|
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"id": "cbc2539f",
|
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"metadata": {},
|
569 |
"outputs": [
|
570 |
{
|
571 |
"data": {
|
572 |
"text/plain": [
|
573 |
+
"(0.07993515937860181,\n",
|
574 |
+
" 0.035626554824269824,\n",
|
575 |
+
" 0.07536807168546154,\n",
|
576 |
+
" 0.03487760945087219)"
|
577 |
]
|
578 |
},
|
579 |
+
"execution_count": 26,
|
580 |
"metadata": {},
|
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"output_type": "execute_result"
|
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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,
|
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"id": "4c543d0c",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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+
"model_id": "7270a78ff7874222b18f538069750bc1",
|
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"version_major": 2,
|
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"version_minor": 0
|
600 |
},
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},
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"metadata": {},
|
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"output_type": "display_data"
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}
|
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],
|
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"source": [
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},
|
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{
|
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"cell_type": "code",
|
615 |
+
"execution_count": null,
|
616 |
"id": "05ec385e",
|
617 |
"metadata": {},
|
618 |
+
"outputs": [],
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|
619 |
"source": [
|
620 |
"model_tf = TFWav2Vec2ForCTC.from_pretrained(\n",
|
621 |
+
" './checkpoint-115000',\n",
|
622 |
" ctc_loss_reduction=\"mean\",\n",
|
623 |
" pad_token_id=tokenizer.pad_token_id,\n",
|
624 |
" vocab_size=len(tokenizer),\n",
|
|
|
628 |
},
|
629 |
{
|
630 |
"cell_type": "code",
|
631 |
+
"execution_count": null,
|
632 |
"id": "e0f3f749",
|
633 |
"metadata": {},
|
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+
"outputs": [],
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|
635 |
"source": [
|
636 |
"model_tf.push_to_hub('wav2vec2-xls-r-300m-mixed', organization='mesolitica')"
|
637 |
]
|