Kabyle_xlsr / README.md

Librarian Bot: Add base_model information to model

f697f1e about 1 year ago

6.72 kB

	---
	language:
	- kab
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- mozilla-foundation/common_voice_8_0
	- generated_from_trainer
	- sw
	- robust-speech-event
	- model_for_talk
	- hf-asr-leaderboard
	datasets:
	- mozilla-foundation/common_voice_8_0
	base_model: facebook/wav2vec2-xls-r-300m
	model-index:
	- name: Akashpb13/Kabyle_xlsr
	results:
	- task:
	type: automatic-speech-recognition
	name: Automatic Speech Recognition
	dataset:
	name: Common Voice 8
	type: mozilla-foundation/common_voice_8_0
	args: kab
	metrics:
	- type: wer
	value: 0.3188425282720088
	name: Test WER
	- type: cer
	value: 0.09443079928558358
	name: Test CER
	---

	# Akashpb13/Kabyle_xlsr

	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_7_0 - hu dataset.
	It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
	- Loss: 0.159032
	- Wer: 0.187934
	## Model description
	"facebook/wav2vec2-xls-r-300m" was finetuned.

	## Intended uses & limitations
	More information needed
	## Training and evaluation data
	Training data -
	Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
	Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

	## Training procedure
	For creating the training dataset, all possible datasets were appended and 90-10 split was used.

	### Training hyperparameters

	The following hyperparameters were used during training:

	- learning_rate: 0.000096
	- train_batch_size: 8
	- seed: 13
	- gradient_accumulation_steps: 4
	- lr_scheduler_type: cosine_with_restarts
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 30
	- mixed_precision_training: Native AMP


	### Training results
	\| Step \| Training Loss \| Validation Loss \| Wer \|
	\|-------\|---------------\|-----------------\|----------\|
	\| 500 \| 7.199800 \| 3.130564 \| 1.000000 \|
	\| 1000 \| 1.570200 \| 0.718097 \| 0.734682 \|
	\| 1500 \| 0.850800 \| 0.524227 \| 0.640532 \|
	\| 2000 \| 0.712200 \| 0.468694 \| 0.603454 \|
	\| 2500 \| 0.651200 \| 0.413833 \| 0.573025 \|
	\| 3000 \| 0.603100 \| 0.403680 \| 0.552847 \|
	\| 3500 \| 0.553300 \| 0.372638 \| 0.541719 \|
	\| 4000 \| 0.537200 \| 0.353759 \| 0.531191 \|
	\| 4500 \| 0.506300 \| 0.359109 \| 0.519601 \|
	\| 5000 \| 0.479600 \| 0.343937 \| 0.511336 \|
	\| 5500 \| 0.479800 \| 0.338214 \| 0.503948 \|
	\| 6000 \| 0.449500 \| 0.332600 \| 0.495221 \|
	\| 6500 \| 0.439200 \| 0.323905 \| 0.492635 \|
	\| 7000 \| 0.434900 \| 0.310417 \| 0.484555 \|
	\| 7500 \| 0.403200 \| 0.311247 \| 0.483262 \|
	\| 8000 \| 0.401500 \| 0.295637 \| 0.476566 \|
	\| 8500 \| 0.397000 \| 0.301321 \| 0.471672 \|
	\| 9000 \| 0.371600 \| 0.295639 \| 0.468440 \|
	\| 9500 \| 0.370700 \| 0.294039 \| 0.468902 \|
	\| 10000 \| 0.364900 \| 0.291195 \| 0.468440 \|
	\| 10500 \| 0.348300 \| 0.284898 \| 0.461098 \|
	\| 11000 \| 0.350100 \| 0.281764 \| 0.459805 \|
	\| 11500 \| 0.336900 \| 0.291022 \| 0.461606 \|
	\| 12000 \| 0.330700 \| 0.280467 \| 0.455234 \|
	\| 12500 \| 0.322500 \| 0.271714 \| 0.452694 \|
	\| 13000 \| 0.307400 \| 0.289519 \| 0.455465 \|
	\| 13500 \| 0.309300 \| 0.281922 \| 0.451217 \|
	\| 14000 \| 0.304800 \| 0.271514 \| 0.452186 \|
	\| 14500 \| 0.288100 \| 0.286801 \| 0.446830 \|
	\| 15000 \| 0.293200 \| 0.276309 \| 0.445399 \|
	\| 15500 \| 0.289800 \| 0.287188 \| 0.446230 \|
	\| 16000 \| 0.274800 \| 0.286406 \| 0.441243 \|
	\| 16500 \| 0.271700 \| 0.284754 \| 0.441520 \|
	\| 17000 \| 0.262500 \| 0.275431 \| 0.442167 \|
	\| 17500 \| 0.255500 \| 0.276575 \| 0.439858 \|
	\| 18000 \| 0.260200 \| 0.269911 \| 0.435425 \|
	\| 18500 \| 0.250600 \| 0.270519 \| 0.434686 \|
	\| 19000 \| 0.243300 \| 0.267655 \| 0.437826 \|
	\| 19500 \| 0.240600 \| 0.277109 \| 0.431731 \|
	\| 20000 \| 0.237200 \| 0.266622 \| 0.433994 \|
	\| 20500 \| 0.231300 \| 0.273015 \| 0.428868 \|
	\| 21000 \| 0.227200 \| 0.263024 \| 0.430161 \|
	\| 21500 \| 0.220400 \| 0.272880 \| 0.429607 \|
	\| 22000 \| 0.218600 \| 0.272340 \| 0.426883 \|
	\| 22500 \| 0.213100 \| 0.277066 \| 0.428407 \|
	\| 23000 \| 0.205000 \| 0.278404 \| 0.424020 \|
	\| 23500 \| 0.200900 \| 0.270877 \| 0.418987 \|
	\| 24000 \| 0.199000 \| 0.289120 \| 0.425821 \|
	\| 24500 \| 0.196100 \| 0.275831 \| 0.424066 \|
	\| 25000 \| 0.191100 \| 0.282822 \| 0.421850 \|
	\| 25500 \| 0.190100 \| 0.275820 \| 0.418248 \|
	\| 26000 \| 0.178800 \| 0.279208 \| 0.419125 \|
	\| 26500 \| 0.183100 \| 0.271464 \| 0.419218 \|
	\| 27000 \| 0.177400 \| 0.280869 \| 0.419680 \|
	\| 27500 \| 0.171800 \| 0.279593 \| 0.414924 \|
	\| 28000 \| 0.172900 \| 0.276949 \| 0.417648 \|
	\| 28500 \| 0.164900 \| 0.283491 \| 0.417786 \|
	\| 29000 \| 0.164800 \| 0.283122 \| 0.416078 \|
	\| 29500 \| 0.165500 \| 0.281969 \| 0.415801 \|
	\| 30000 \| 0.163800 \| 0.283319 \| 0.412753 \|
	\| 30500 \| 0.153500 \| 0.285702 \| 0.414046 \|
	\| 31000 \| 0.156500 \| 0.285041 \| 0.412615 \|
	\| 31500 \| 0.150900 \| 0.284336 \| 0.413723 \|
	\| 32000 \| 0.151800 \| 0.285922 \| 0.412292 \|
	\| 32500 \| 0.149200 \| 0.289461 \| 0.412153 \|
	\| 33000 \| 0.145400 \| 0.291322 \| 0.409567 \|
	\| 33500 \| 0.145600 \| 0.294361 \| 0.409614 \|
	\| 34000 \| 0.144200 \| 0.290686 \| 0.409059 \|
	\| 34500 \| 0.143400 \| 0.289474 \| 0.409844 \|
	\| 35000 \| 0.143500 \| 0.290340 \| 0.408367 \|
	\| 35500 \| 0.143200 \| 0.289581 \| 0.407351 \|
	\| 36000 \| 0.138400 \| 0.292782 \| 0.408736 \|
	\| 36500 \| 0.137900 \| 0.289108 \| 0.408044 \|
	\| 37000 \| 0.138200 \| 0.292127 \| 0.407166 \|
	\| 37500 \| 0.134600 \| 0.291797 \| 0.408413 \|
	\| 38000 \| 0.139800 \| 0.290056 \| 0.408090 \|
	\| 38500 \| 0.136500 \| 0.291198 \| 0.408090 \|
	\| 39000 \| 0.137700 \| 0.289696 \| 0.408044 \|


	### Framework versions
	- Transformers 4.16.0.dev0
	- Pytorch 1.10.0+cu102
	- Datasets 1.18.3
	- Tokenizers 0.10.3

	#### Evaluation Commands

	1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`

	```bash
	python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test
	```

	---
	language:
	- kab
	license: apache-2.0
	tags:
	- automatic-speech-recognition
	- mozilla-foundation/common_voice_8_0
	- generated_from_trainer
	- sw
	- robust-speech-event
	- model_for_talk
	- hf-asr-leaderboard
	datasets:
	- mozilla-foundation/common_voice_8_0
	base_model: facebook/wav2vec2-xls-r-300m
	model-index:
	- name: Akashpb13/Kabyle_xlsr
	results:
	- task:
	type: automatic-speech-recognition
	name: Automatic Speech Recognition
	dataset:
	name: Common Voice 8
	type: mozilla-foundation/common_voice_8_0
	args: kab
	metrics:
	- type: wer
	value: 0.3188425282720088
	name: Test WER
	- type: cer
	value: 0.09443079928558358
	name: Test CER
	---

	# Akashpb13/Kabyle_xlsr

	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_7_0 - hu dataset.
	It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets):
	- Loss: 0.159032
	- Wer: 0.187934
	## Model description
	"facebook/wav2vec2-xls-r-300m" was finetuned.

	## Intended uses & limitations
	More information needed
	## Training and evaluation data
	Training data -
	Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset.
	Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0

	## Training procedure
	For creating the training dataset, all possible datasets were appended and 90-10 split was used.

	### Training hyperparameters

	The following hyperparameters were used during training:

	- learning_rate: 0.000096
	- train_batch_size: 8
	- seed: 13
	- gradient_accumulation_steps: 4
	- lr_scheduler_type: cosine_with_restarts
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 30
	- mixed_precision_training: Native AMP


	### Training results
	\| Step \| Training Loss \| Validation Loss \| Wer \|
	\|-------\|---------------\|-----------------\|----------\|
	\| 500 \| 7.199800 \| 3.130564 \| 1.000000 \|
	\| 1000 \| 1.570200 \| 0.718097 \| 0.734682 \|
	\| 1500 \| 0.850800 \| 0.524227 \| 0.640532 \|
	\| 2000 \| 0.712200 \| 0.468694 \| 0.603454 \|
	\| 2500 \| 0.651200 \| 0.413833 \| 0.573025 \|
	\| 3000 \| 0.603100 \| 0.403680 \| 0.552847 \|
	\| 3500 \| 0.553300 \| 0.372638 \| 0.541719 \|
	\| 4000 \| 0.537200 \| 0.353759 \| 0.531191 \|
	\| 4500 \| 0.506300 \| 0.359109 \| 0.519601 \|
	\| 5000 \| 0.479600 \| 0.343937 \| 0.511336 \|
	\| 5500 \| 0.479800 \| 0.338214 \| 0.503948 \|
	\| 6000 \| 0.449500 \| 0.332600 \| 0.495221 \|
	\| 6500 \| 0.439200 \| 0.323905 \| 0.492635 \|
	\| 7000 \| 0.434900 \| 0.310417 \| 0.484555 \|
	\| 7500 \| 0.403200 \| 0.311247 \| 0.483262 \|
	\| 8000 \| 0.401500 \| 0.295637 \| 0.476566 \|
	\| 8500 \| 0.397000 \| 0.301321 \| 0.471672 \|
	\| 9000 \| 0.371600 \| 0.295639 \| 0.468440 \|
	\| 9500 \| 0.370700 \| 0.294039 \| 0.468902 \|
	\| 10000 \| 0.364900 \| 0.291195 \| 0.468440 \|
	\| 10500 \| 0.348300 \| 0.284898 \| 0.461098 \|
	\| 11000 \| 0.350100 \| 0.281764 \| 0.459805 \|
	\| 11500 \| 0.336900 \| 0.291022 \| 0.461606 \|
	\| 12000 \| 0.330700 \| 0.280467 \| 0.455234 \|
	\| 12500 \| 0.322500 \| 0.271714 \| 0.452694 \|
	\| 13000 \| 0.307400 \| 0.289519 \| 0.455465 \|
	\| 13500 \| 0.309300 \| 0.281922 \| 0.451217 \|
	\| 14000 \| 0.304800 \| 0.271514 \| 0.452186 \|
	\| 14500 \| 0.288100 \| 0.286801 \| 0.446830 \|
	\| 15000 \| 0.293200 \| 0.276309 \| 0.445399 \|
	\| 15500 \| 0.289800 \| 0.287188 \| 0.446230 \|
	\| 16000 \| 0.274800 \| 0.286406 \| 0.441243 \|
	\| 16500 \| 0.271700 \| 0.284754 \| 0.441520 \|
	\| 17000 \| 0.262500 \| 0.275431 \| 0.442167 \|
	\| 17500 \| 0.255500 \| 0.276575 \| 0.439858 \|
	\| 18000 \| 0.260200 \| 0.269911 \| 0.435425 \|
	\| 18500 \| 0.250600 \| 0.270519 \| 0.434686 \|
	\| 19000 \| 0.243300 \| 0.267655 \| 0.437826 \|
	\| 19500 \| 0.240600 \| 0.277109 \| 0.431731 \|
	\| 20000 \| 0.237200 \| 0.266622 \| 0.433994 \|
	\| 20500 \| 0.231300 \| 0.273015 \| 0.428868 \|
	\| 21000 \| 0.227200 \| 0.263024 \| 0.430161 \|
	\| 21500 \| 0.220400 \| 0.272880 \| 0.429607 \|
	\| 22000 \| 0.218600 \| 0.272340 \| 0.426883 \|
	\| 22500 \| 0.213100 \| 0.277066 \| 0.428407 \|
	\| 23000 \| 0.205000 \| 0.278404 \| 0.424020 \|
	\| 23500 \| 0.200900 \| 0.270877 \| 0.418987 \|
	\| 24000 \| 0.199000 \| 0.289120 \| 0.425821 \|
	\| 24500 \| 0.196100 \| 0.275831 \| 0.424066 \|
	\| 25000 \| 0.191100 \| 0.282822 \| 0.421850 \|
	\| 25500 \| 0.190100 \| 0.275820 \| 0.418248 \|
	\| 26000 \| 0.178800 \| 0.279208 \| 0.419125 \|
	\| 26500 \| 0.183100 \| 0.271464 \| 0.419218 \|
	\| 27000 \| 0.177400 \| 0.280869 \| 0.419680 \|
	\| 27500 \| 0.171800 \| 0.279593 \| 0.414924 \|
	\| 28000 \| 0.172900 \| 0.276949 \| 0.417648 \|
	\| 28500 \| 0.164900 \| 0.283491 \| 0.417786 \|
	\| 29000 \| 0.164800 \| 0.283122 \| 0.416078 \|
	\| 29500 \| 0.165500 \| 0.281969 \| 0.415801 \|
	\| 30000 \| 0.163800 \| 0.283319 \| 0.412753 \|
	\| 30500 \| 0.153500 \| 0.285702 \| 0.414046 \|
	\| 31000 \| 0.156500 \| 0.285041 \| 0.412615 \|
	\| 31500 \| 0.150900 \| 0.284336 \| 0.413723 \|
	\| 32000 \| 0.151800 \| 0.285922 \| 0.412292 \|
	\| 32500 \| 0.149200 \| 0.289461 \| 0.412153 \|
	\| 33000 \| 0.145400 \| 0.291322 \| 0.409567 \|
	\| 33500 \| 0.145600 \| 0.294361 \| 0.409614 \|
	\| 34000 \| 0.144200 \| 0.290686 \| 0.409059 \|
	\| 34500 \| 0.143400 \| 0.289474 \| 0.409844 \|
	\| 35000 \| 0.143500 \| 0.290340 \| 0.408367 \|
	\| 35500 \| 0.143200 \| 0.289581 \| 0.407351 \|
	\| 36000 \| 0.138400 \| 0.292782 \| 0.408736 \|
	\| 36500 \| 0.137900 \| 0.289108 \| 0.408044 \|
	\| 37000 \| 0.138200 \| 0.292127 \| 0.407166 \|
	\| 37500 \| 0.134600 \| 0.291797 \| 0.408413 \|
	\| 38000 \| 0.139800 \| 0.290056 \| 0.408090 \|
	\| 38500 \| 0.136500 \| 0.291198 \| 0.408090 \|
	\| 39000 \| 0.137700 \| 0.289696 \| 0.408044 \|


	### Framework versions
	- Transformers 4.16.0.dev0
	- Pytorch 1.10.0+cu102
	- Datasets 1.18.3
	- Tokenizers 0.10.3

	#### Evaluation Commands

	1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`

	```bash
	python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test
	```