Training in progress, step 500
Browse files- .ipynb_checkpoints/eval-checkpoint.py +128 -0
- .ipynb_checkpoints/run-checkpoint.sh +38 -0
- config.json +3 -3
- log_mozilla-foundation_common_voice_8_0_ky_test_predictions.txt +0 -0
- log_mozilla-foundation_common_voice_8_0_ky_test_targets.txt +0 -0
- pytorch_model.bin +1 -1
- run.sh +5 -5
- runs/Feb04_19-31-13_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/1644003121.6455085/events.out.tfevents.1644003121.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1462870.1 +3 -0
- runs/Feb04_19-31-13_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/events.out.tfevents.1644003121.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1462870.0 +3 -0
- runs/Feb04_19-35-31_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/1644003377.3563116/events.out.tfevents.1644003377.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1464599.1 +3 -0
- runs/Feb04_19-35-31_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/events.out.tfevents.1644003377.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1464599.0 +3 -0
- special_tokens_map.json +1 -1
- training_args.bin +1 -1
.ipynb_checkpoints/eval-checkpoint.py
ADDED
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#!/usr/bin/env python3
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import argparse
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import re
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from typing import Dict
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from datasets import Audio, Dataset, load_dataset, load_metric
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from transformers import AutoFeatureExtractor, pipeline
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def log_results(result: Dataset, args: Dict[str, str]):
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"""DO NOT CHANGE. This function computes and logs the result metrics."""
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log_outputs = args.log_outputs
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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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# load metric
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wer = load_metric("wer")
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cer = load_metric("cer")
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# compute metrics
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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# print & log results
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result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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print(result_str)
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with open(f"{dataset_id}_eval_results.txt", "w") as f:
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f.write(result_str)
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# log all results in text file. Possibly interesting for analysis
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if log_outputs is not None:
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pred_file = f"log_{dataset_id}_predictions.txt"
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target_file = f"log_{dataset_id}_targets.txt"
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with open(pred_file, "w") as p, open(target_file, "w") as t:
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# mapping function to write output
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def write_to_file(batch, i):
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p.write(f"{i}" + "\n")
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p.write(batch["prediction"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["target"] + "\n")
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result.map(write_to_file, with_indices=True)
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def normalize_text(text: str) -> str:
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"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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chars_to_ignore_regex = '[!"%,.:;?\\_|©«¬»،؛؟‒–—’“”„…‹›−☺♂�\\\\-]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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text = re.sub(chars_to_ignore_regex, "", text.lower())
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# In addition, we can normalize the target text, e.g. removing new lines characters etc...
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# note that order is important here!
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token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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for t in token_sequences_to_ignore:
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text = " ".join(text.split(t))
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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# for testing: only process the first two examples as a test
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# dataset = dataset.select(range(10))
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# load processor
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feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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sampling_rate = feature_extractor.sampling_rate
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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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asr = pipeline("automatic-speech-recognition", model=args.model_id)
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(
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batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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)
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batch["prediction"] = prediction["text"]
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batch["target"] = normalize_text(batch["sentence"])
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return batch
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# run inference on all examples
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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# compute and log_results
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# do not change function below
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log_results(result, args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset",
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type=str,
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required=True,
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help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
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)
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parser.add_argument(
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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)
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parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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parser.add_argument(
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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)
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parser.add_argument(
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
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)
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parser.add_argument(
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"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
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)
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args = parser.parse_args()
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main(args)
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.ipynb_checkpoints/run-checkpoint.sh
ADDED
@@ -0,0 +1,38 @@
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python kyrgiz/run_speech_recognition_ctc.py \
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--dataset_name="mozilla-foundation/common_voice_8_0" \
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--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
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--dataset_config_name="ky" \
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--train_split_name="train+validation[:50%]" \
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--eval_split_name="validation[50%:]" \
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--output_dir="./xls-r-kyrgiz-cv8" \
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--overwrite_output_dir \
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--num_train_epochs="50" \
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--per_device_train_batch_size="16" \
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--per_device_eval_batch_size="8" \
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--gradient_accumulation_steps="4" \
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--learning_rate="1e-4" \
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--warmup_steps="250" \
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--length_column_name="input_length" \
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--evaluation_strategy="steps" \
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--text_column_name="sentence" \
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--chars_to_ignore , ? . ! \- \; \: \\ _ \| ‒ ☺ ♂ © « ¬ » \" „ “ % ” � — ’ ، ؛ ؟ ‹ › − … – \
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--eval_metrics="wer" \
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--save_steps="500" \
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--eval_steps="500" \
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--logging_steps="100" \
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--min_duration_in_seconds="0.2" \
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--layerdrop="0.01" \
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--activation_dropout="0.1" \
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--save_total_limit="3" \
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--freeze_feature_encoder \
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--feat_proj_dropout="0.01" \
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--mask_time_prob="0.50" \
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--mask_time_length="10" \
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--mask_feature_prob="0.25" \
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--mask_feature_length="64" \
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--gradient_checkpointing \
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--use_auth_token \
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--fp16 \
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--group_by_length \
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--do_train --do_eval \
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--push_to_hub
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config.json
CHANGED
@@ -49,7 +49,7 @@
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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-
"feat_proj_dropout": 0.
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"hidden_act": "gelu",
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@@ -58,13 +58,13 @@
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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-
"layerdrop": 0.
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"mask_feature_length": 64,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.25,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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-
"mask_time_prob": 0.
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"model_type": "wav2vec2",
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"num_adapter_layers": 3,
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"num_attention_heads": 16,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.01,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"hidden_act": "gelu",
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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+
"layerdrop": 0.01,
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"mask_feature_length": 64,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.25,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.5,
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"model_type": "wav2vec2",
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"num_adapter_layers": 3,
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"num_attention_heads": 16,
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log_mozilla-foundation_common_voice_8_0_ky_test_predictions.txt
ADDED
The diff for this file is too large to render.
See raw diff
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log_mozilla-foundation_common_voice_8_0_ky_test_targets.txt
ADDED
The diff for this file is too large to render.
See raw diff
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pytorch_model.bin
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1262095857
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version https://git-lfs.github.com/spec/v1
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oid sha256:6199b399576e56ccf0b75f137f2c3b014f6ed6fc8036caf25a21c670c49ffc76
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size 1262095857
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run.sh
CHANGED
@@ -6,12 +6,12 @@ python kyrgiz/run_speech_recognition_ctc.py \
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--eval_split_name="validation[50%:]" \
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--output_dir="./xls-r-kyrgiz-cv8" \
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--overwrite_output_dir \
|
9 |
-
--num_train_epochs="
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--per_device_train_batch_size="16" \
|
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--per_device_eval_batch_size="8" \
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--gradient_accumulation_steps="4" \
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--learning_rate="1e-4" \
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-
--warmup_steps="
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--length_column_name="input_length" \
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--evaluation_strategy="steps" \
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--text_column_name="sentence" \
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--eval_steps="500" \
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--logging_steps="100" \
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--min_duration_in_seconds="0.2" \
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-
--layerdrop="0.
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--activation_dropout="0.1" \
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--save_total_limit="3" \
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--freeze_feature_encoder \
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-
--feat_proj_dropout="0.
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-
--mask_time_prob="0.
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--mask_time_length="10" \
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--mask_feature_prob="0.25" \
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--mask_feature_length="64" \
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--eval_split_name="validation[50%:]" \
|
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--output_dir="./xls-r-kyrgiz-cv8" \
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--overwrite_output_dir \
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+
--num_train_epochs="50" \
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10 |
--per_device_train_batch_size="16" \
|
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--per_device_eval_batch_size="8" \
|
12 |
--gradient_accumulation_steps="4" \
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--learning_rate="1e-4" \
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+
--warmup_steps="250" \
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--length_column_name="input_length" \
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--evaluation_strategy="steps" \
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--text_column_name="sentence" \
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|
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--eval_steps="500" \
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--logging_steps="100" \
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--min_duration_in_seconds="0.2" \
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+
--layerdrop="0.01" \
|
25 |
--activation_dropout="0.1" \
|
26 |
--save_total_limit="3" \
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27 |
--freeze_feature_encoder \
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28 |
+
--feat_proj_dropout="0.01" \
|
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+
--mask_time_prob="0.50" \
|
30 |
--mask_time_length="10" \
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31 |
--mask_feature_prob="0.25" \
|
32 |
--mask_feature_length="64" \
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runs/Feb04_19-31-13_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/1644003121.6455085/events.out.tfevents.1644003121.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1462870.1
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version https://git-lfs.github.com/spec/v1
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runs/Feb04_19-31-13_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/events.out.tfevents.1644003121.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1462870.0
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version https://git-lfs.github.com/spec/v1
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size 4756
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runs/Feb04_19-35-31_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/1644003377.3563116/events.out.tfevents.1644003377.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1464599.1
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version https://git-lfs.github.com/spec/v1
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size 4802
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runs/Feb04_19-35-31_job-699ba53c-fea9-4eb2-81af-a97f440eaa45/events.out.tfevents.1644003377.job-699ba53c-fea9-4eb2-81af-a97f440eaa45.1464599.0
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:d99720c701214c1653a05e1396a33a187eb008fdc29e97e62c9a9607f1ad7823
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size 5856
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special_tokens_map.json
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@@ -1 +1 @@
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}
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training_args.bin
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 3055
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version https://git-lfs.github.com/spec/v1
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oid sha256:2b4de827673e3dfe665950f44fa7245a141f080f380ad5cea7b1833a16096357
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size 3055
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