shahukareem
commited on
Commit
•
060a9f0
1
Parent(s):
0179087
Upload lm-boosted decoder
Browse files- .gitattributes +1 -0
- added_tokens.json +1 -0
- alphabet.json +1 -0
- config.json +108 -0
- eval.py +137 -0
- language_model/5gram.bin +3 -0
- language_model/attrs.json +1 -0
- language_model/unigrams.txt +3 -0
- preprocessor_config.json +10 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +1 -0
- tokenizer_config.json +1 -0
- vocab.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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language_model/unigrams.txt filter=lfs diff=lfs merge=lfs -text
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added_tokens.json
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{"<s>": 54, "</s>": 55}
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alphabet.json
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{"labels": [" ", "\u0651", "\u0780", "\u0781", "\u0782", "\u0783", "\u0784", "\u0785", "\u0786", "\u0787", "\u0788", "\u0789", "\u078a", "\u078b", "\u078c", "\u078d", "\u078e", "\u078f", "\u0790", "\u0791", "\u0792", "\u0793", "\u0794", "\u0795", "\u0796", "\u0797", "\u0798", "\u0799", "\u079a", "\u079b", "\u079d", "\u079e", "\u079f", "\u07a0", "\u07a1", "\u07a2", "\u07a3", "\u07a4", "\u07a5", "\u07a6", "\u07a7", "\u07a8", "\u07a9", "\u07aa", "\u07ab", "\u07ac", "\u07ad", "\u07ae", "\u07af", "\u07b0", "\ufdf2", "\ufdfa", "\u2047", "", "<s>", "</s>"], "is_bpe": false}
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config.json
ADDED
@@ -0,0 +1,108 @@
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{
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"_name_or_path": "facebook/wav2vec2-xls-r-1b",
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"activation_dropout": 0.0,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 1024,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": false,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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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": 0.1,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 1280,
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"initializer_range": 0.02,
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"intermediate_size": 5120,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.1,
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"mask_feature_length": 10,
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"mask_feature_min_masks": 0,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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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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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 48,
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"num_negatives": 100,
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"output_hidden_size": 1280,
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"pad_token_id": 53,
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"proj_codevector_dim": 1024,
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"tdnn_dilation": [
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1,
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2,
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3,
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1,
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1
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],
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"tdnn_dim": [
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512,
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512,
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512,
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512,
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1500
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],
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"tdnn_kernel": [
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5,
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3,
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3,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.17.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 56,
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"xvector_output_dim": 512
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}
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eval.py
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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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import torch
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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 = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\،\.\؟\!\'\"\–\’\ل\ه\ا]' # 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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if args.device is None:
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args.device = 0 if torch.cuda.is_available() else -1
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asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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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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parser.add_argument(
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"--device",
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type=int,
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default=None,
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help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
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)
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args = parser.parse_args()
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main(args)
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language_model/5gram.bin
ADDED
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:30735e294875ada05ab087dcde92e7afbe597565f371d6b9499473c0b5d44d08
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size 4227352606
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language_model/attrs.json
ADDED
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{"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true}
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language_model/unigrams.txt
ADDED
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version https://git-lfs.github.com/spec/v1
|
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+
oid sha256:42106df05301327c86460eefd72cb8e2d66f5a06f34e389f164b960db4be38e5
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size 55019503
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preprocessor_config.json
ADDED
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{
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"do_normalize": true,
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"feature_extractor_type": "Wav2Vec2FeatureExtractor",
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"feature_size": 1,
|
5 |
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"padding_side": "right",
|
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"padding_value": 0.0,
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"processor_class": "Wav2Vec2ProcessorWithLM",
|
8 |
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"return_attention_mask": true,
|
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"sampling_rate": 16000
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}
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pytorch_model.bin
ADDED
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version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c9be2a9e9a5fe00723a32a4cdac2df36a54aef5728bc8d57265c0cd135c43bfd
|
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+
size 3850599665
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special_tokens_map.json
ADDED
@@ -0,0 +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}]}
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tokenizer_config.json
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+
{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "/content/wav2vec2-xls-r-1b-dv-with-lm-v2", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2ProcessorWithLM"}
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vocab.json
ADDED
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+
{"ّ": 1, "ހ": 2, "ށ": 3, "ނ": 4, "ރ": 5, "ބ": 6, "ޅ": 7, "ކ": 8, "އ": 9, "ވ": 10, "މ": 11, "ފ": 12, "ދ": 13, "ތ": 14, "ލ": 15, "ގ": 16, "ޏ": 17, "ސ": 18, "ޑ": 19, "ޒ": 20, "ޓ": 21, "ޔ": 22, "ޕ": 23, "ޖ": 24, "ޗ": 25, "ޘ": 26, "ޙ": 27, "ޚ": 28, "ޛ": 29, "ޝ": 30, "ޞ": 31, "ޟ": 32, "ޠ": 33, "ޡ": 34, "ޢ": 35, "ޣ": 36, "ޤ": 37, "ޥ": 38, "ަ": 39, "ާ": 40, "ި": 41, "ީ": 42, "ު": 43, "ޫ": 44, "ެ": 45, "ޭ": 46, "ޮ": 47, "ޯ": 48, "ް": 49, "ﷲ": 50, "ﷺ": 51, "|": 0, "[UNK]": 52, "[PAD]": 53}
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