lighteternal
commited on
Commit
•
2c4a267
1
Parent(s):
a9d1c20
Added new model trained on 60 epochs
Browse files- ASR_Inference.ipynb +276 -55
- Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers.ipynb +0 -0
- README.md +13 -8
- logs.png +0 -0
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/config.json +0 -0
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/optimizer.pt +2 -2
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/preprocessor_config.json +0 -0
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/pytorch_model.bin +1 -1
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/scheduler.pt +1 -1
- wav2vec2-large-xlsr-greek/checkpoint-18400/trainer_state.json +660 -0
- wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/training_args.bin +1 -1
- wav2vec2-large-xlsr-greek/checkpoint-9200/trainer_state.json +0 -338
- wav2vec2-large-xlsr-greek/vocab.json +1 -1
ASR_Inference.ipynb
CHANGED
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"source": [
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"model = Wav2Vec2ForCTC.from_pretrained(\"wav2vec2-large-xlsr-greek/checkpoint-
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"processor = Wav2Vec2Processor.from_pretrained(\"wav2vec2-large-xlsr-greek/\")"
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"source": [
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"common_voice_test = load_dataset(\"common_voice\", \"el\", data_dir=\"cv-corpus-6.1-2020-12-11\", split=\"test\")"
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"common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=[\"sentence\"])"
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"common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)"
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"source": [
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"common_voice_test = common_voice_test.map(resample, num_proc=8)"
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"source": [
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"common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)"
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"outputs": [
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"source": [
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"common_voice_test_transcription = load_dataset(\"common_voice\", \"el\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
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"outputs": [],
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"source": [
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"# Change this value to try inference on different CommonVoice extracts\n",
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"example =
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"\n",
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"input_dict = processor(common_voice_test[\"input_values\"][example], return_tensors=\"pt\", sampling_rate=16_000, padding=True)\n",
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"\n",
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"outputs": [
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"source": [
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"print(\"Prediction:\")\n",
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"print(processor.decode(pred_ids[0]))\n",
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"#
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"\n",
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"print(\"\\nReference:\")\n",
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"print(common_voice_test_transcription[\"sentence\"][example].lower())\n",
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"#
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"execution_count": null,
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"metadata": {
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-03-14T18:07:15.328900Z",
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"start_time": "2021-03-14T18:07:15.326838Z"
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"outputs": [],
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"execution_count": 16,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-03-14T18:07:15.933957Z",
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"start_time": "2021-03-14T18:07:15.927789Z"
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"outputs": [],
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-03-14T18:07:22.624226Z",
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"start_time": "2021-03-14T18:07:16.402381Z"
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Special tokens have been added in the vocabulary, make sure the associated word embedding are fine-tuned or trained.\n"
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]
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}
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],
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"source": [
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"model = Wav2Vec2ForCTC.from_pretrained(\"wav2vec2-large-xlsr-greek/checkpoint-18400/\").to(\"cuda\")\n",
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"processor = Wav2Vec2Processor.from_pretrained(\"wav2vec2-large-xlsr-greek/\")"
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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": 18,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-03-14T18:07:25.473609Z",
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"start_time": "2021-03-14T18:07:22.644765Z"
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using custom data configuration el-afd0a157f05ee080\n",
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"Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n"
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]
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}
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],
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"source": [
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"common_voice_test = load_dataset(\"common_voice\", \"el\", data_dir=\"cv-corpus-6.1-2020-12-11\", split=\"test\")"
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]
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},
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{
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"execution_count": 19,
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"metadata": {
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"end_time": "2021-03-14T18:07:25.504511Z",
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"start_time": "2021-03-14T18:07:25.500688Z"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-0ce2ebca66096fff.arrow\n"
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]
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}
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],
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"source": [
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"common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=[\"sentence\"])"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"start_time": "2021-03-14T18:07:25.568808Z"
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-38a09981767eff59.arrow\n"
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]
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}
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],
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"source": [
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"common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)"
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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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"metadata": {
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"end_time": "2021-03-14T18:07:26.404914Z",
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"output_type": "stream",
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"text": [
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"Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-ba8c6dd59eb8ccf2.arrow\n"
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"text": [
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"Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-2e240883a5f827fd.arrow\n"
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"source": [
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"Loading cached processed dataset at /home/earendil/.cache/huggingface/datasets/common_voice/el-afd0a157f05ee080/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564/cache-118401c99df7b83c.arrow\n"
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"source": [
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"common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)"
|
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]
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},
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"metadata": {
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"end_time": "2021-03-14T18:07:29.428864Z",
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"name": "stderr",
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"text": [
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"Using custom data configuration el-ac779bf2c9f7c09b\n",
|
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+
"Reusing dataset common_voice (/home/earendil/.cache/huggingface/datasets/common_voice/el-ac779bf2c9f7c09b/6.1.0/32954a9015faa0d840f6c6894938545c5d12bc5d8936a80079af74bf50d71564)\n"
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],
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"source": [
|
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"common_voice_test_transcription = load_dataset(\"common_voice\", \"el\", data_dir=\"./cv-corpus-6.1-2020-12-11\", split=\"test\")"
|
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]
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},
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"ExecuteTime": {
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"start_time": "2021-03-14T18:07:29.451275Z"
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}
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"outputs": [],
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"source": [
|
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"# Change this value to try inference on different CommonVoice extracts\n",
|
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+
"example = 678\n",
|
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"\n",
|
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"input_dict = processor(common_voice_test[\"input_values\"][example], return_tensors=\"pt\", sampling_rate=16_000, padding=True)\n",
|
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"\n",
|
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-03-14T18:07:54.742988Z",
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"start_time": "2021-03-14T18:07:54.739626Z"
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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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+
"Prediction:\n",
|
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+
"πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς\n",
|
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+
"\n",
|
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+
"Reference:\n",
|
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+
"πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.\n"
|
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+
]
|
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+
}
|
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+
],
|
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"source": [
|
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"print(\"Prediction:\")\n",
|
441 |
"print(processor.decode(pred_ids[0]))\n",
|
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+
"# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς\n",
|
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"\n",
|
444 |
"print(\"\\nReference:\")\n",
|
445 |
"print(common_voice_test_transcription[\"sentence\"][example].lower())\n",
|
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+
"# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς."
|
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]
|
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"metadata": {
|
Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers.ipynb
CHANGED
The diff for this file is too large to render.
See raw diff
|
|
README.md
CHANGED
@@ -15,15 +15,17 @@ tags:
|
|
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* language: el
|
16 |
* licence: apache-2.0
|
17 |
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets
|
18 |
-
* model: XLSR-Wav2Vec2
|
19 |
* metrics: WER
|
20 |
|
21 |
### Model description
|
22 |
|
23 |
-
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
|
24 |
|
25 |
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.
|
26 |
|
|
|
|
|
27 |
### How to use for inference:
|
28 |
|
29 |
Instructions to test on CommonVoice extracts are provided in the ASR_Inference.ipynb. Snippet also available below:
|
@@ -113,12 +115,11 @@ pred_ids = torch.argmax(logits, dim=-1)
|
|
113 |
|
114 |
print("Prediction:")
|
115 |
print(processor.decode(pred_ids[0]))
|
116 |
-
#
|
117 |
|
118 |
print("\nReference:")
|
119 |
print(common_voice_test_transcription["sentence"][example].lower())
|
120 |
-
#
|
121 |
-
|
122 |
|
123 |
```
|
124 |
|
@@ -131,9 +132,13 @@ Instructions and code to replicate the process are provided in the Fine_Tune_XLS
|
|
131 |
|
132 |
| Metric | Value |
|
133 |
| ----------- | ----------- |
|
134 |
-
| Training Loss | 0.
|
135 |
-
| Validation Loss | 0.
|
136 |
-
| WER | 0.
|
|
|
|
|
|
|
|
|
137 |
|
138 |
|
139 |
### Acknowledgment
|
|
|
15 |
* language: el
|
16 |
* licence: apache-2.0
|
17 |
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets
|
18 |
+
* model: XLSR-Wav2Vec2, trained for 60 epochs
|
19 |
* metrics: WER
|
20 |
|
21 |
### Model description
|
22 |
|
23 |
+
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.
|
24 |
|
25 |
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.
|
26 |
|
27 |
+
This model was trained on Greek CommonVoice speech data (364MB) for 30 epochs on a single NVIDIA RTX 3080, for aprox. 8hrs.
|
28 |
+
|
29 |
### How to use for inference:
|
30 |
|
31 |
Instructions to test on CommonVoice extracts are provided in the ASR_Inference.ipynb. Snippet also available below:
|
|
|
115 |
|
116 |
print("Prediction:")
|
117 |
print(processor.decode(pred_ids[0]))
|
118 |
+
# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς
|
119 |
|
120 |
print("\nReference:")
|
121 |
print(common_voice_test_transcription["sentence"][example].lower())
|
122 |
+
# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.
|
|
|
123 |
|
124 |
```
|
125 |
|
|
|
132 |
|
133 |
| Metric | Value |
|
134 |
| ----------- | ----------- |
|
135 |
+
| Training Loss | 0.0287 |
|
136 |
+
| Validation Loss | 0.6162 |
|
137 |
+
| WER | 0.4287 |
|
138 |
+
|
139 |
+
Full metrics log here:
|
140 |
+
<img src="https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek/raw/main/logs.png" width="600"/>
|
141 |
+
|
142 |
|
143 |
|
144 |
### Acknowledgment
|
logs.png
ADDED
wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/config.json
RENAMED
File without changes
|
wav2vec2-large-xlsr-greek/{checkpoint-9200 → checkpoint-18400}/optimizer.pt
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RENAMED
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RENAMED
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ADDED
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wav2vec2-large-xlsr-greek/vocab.json
CHANGED
@@ -1 +1 @@
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1 |
-
{"
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1 |
+
{"ώ": 0, "γ": 1, "n": 2, "ϋ": 3, "κ": 4, "e": 5, "ξ": 6, "'": 7, "θ": 8, "’": 9, "σ": 10, "η": 11, "ι": 12, "α": 13, "ε": 14, "υ": 15, "v": 16, "μ": 17, "ο": 18, "«": 19, "»": 20, "έ": 21, "ν": 22, "ά": 24, "o": 25, "ζ": 26, "β": 27, "τ": 28, "π": 29, "ή": 30, "ψ": 31, "ΐ": 32, "ό": 33, "h": 34, "ύ": 35, "ω": 36, "´": 37, "χ": 38, "ϊ": 39, "ρ": 40, "a": 41, "ς": 42, "r": 43, "g": 44, "m": 45, "λ": 46, "́": 47, "ί": 48, "φ": 49, "δ": 50, "|": 23, "[UNK]": 51, "[PAD]": 52}
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