Delete wav2vec2-large-xls-r-300m-hi

wav2vec2-large-xls-r-300m-hi/.gitattributes

@@ -1,35 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

wav2vec2-large-xls-r-300m-hi/README.md

@@ -1,146 +0,0 @@
----
-license: apache-2.0
-base_model: facebook/wav2vec2-xls-r-300m
-tags:
-- generated_from_trainer
-metrics:
-- wer
-- cer
-model-index:
-- name: wav2vec2-large-xls-r-300m-hi
-  results: 
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 15
-      type: mozilla-foundation/common_voice_15_0
-      args: hi
-    metrics:
-      - name: Test WER
-        type: wer
-        value: 0.2934
-      - name: Test CER
-        type: cer
-        value: 0.0786
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 8
-      type: mozilla-foundation/common_voice_8_0
-      args: hi
-    metrics:
-      - name: Test WER
-        type: wer
-        value: 0.5209
-      - name: Test CER
-        type: cer
-        value: 0.1790
-datasets:
-- mozilla-foundation/common_voice_15_0
-language:
-- hi
-library_name: transformers
-pipeline_tag: automatic-speech-recognition
----
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# wav2vec2-large-xls-r-300m-hi
-
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3611
-- Wer: 0.2992
-- Cer: 0.0786
-
-View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/wav2vec-2-eval
-
-## Evaluation
-
-```python
-import torch
-from datasets import load_dataset, load_metric
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import librosa
-import unicodedata
-import re
-
-
-test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "hi", split="test")
-wer = load_metric("wer")
-cer = load_metric("cer")
-
-processor = Wav2Vec2Processor.from_pretrained("kingabzpro/wav2vec2-large-xls-r-300m-hi")
-model = Wav2Vec2ForCTC.from_pretrained("kingabzpro/wav2vec2-large-xls-r-300m-hi")
-model.to("cuda")
-
-
-# Preprocessing the datasets.
-def speech_file_to_array_fn(batch):
-    chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\’\'\|\&\–]'
-    remove_en = '[A-Za-z]'
-    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"].lower())
-    batch["sentence"] = re.sub(remove_en, "", batch["sentence"]).lower()
-    batch["sentence"] = unicodedata.normalize("NFKC", batch["sentence"])
-
-    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
-    batch["speech"] = speech_array
-    return batch
-
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def evaluate(batch):
-  inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-  with torch.no_grad():
-      logits = model(inputs.input_values.to("cuda")).logits
-
-      pred_ids = torch.argmax(logits, dim=-1)
-      batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
-      return batch
-
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
-
-print("WER: {}".format(wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-print("CER: {}".format(cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-
-```
-**WER: 0.5209850206372026**
-
-**CER: 0.17902923538230883**
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 32
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 128
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 300
-- num_epochs: 100
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
-|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
-| 7.0431        | 19.05 | 300  | 3.4423          | 1.0    | 1.0    |
-| 2.3233        | 38.1  | 600  | 0.5965          | 0.4757 | 0.1329 |
-| 0.5676        | 57.14 | 900  | 0.3962          | 0.3584 | 0.0954 |
-| 0.3611        | 76.19 | 1200 | 0.3651          | 0.3190 | 0.0820 |
-| 0.2996        | 95.24 | 1500 | 0.3611          | 0.2992 | 0.0786 |
-
-
-### Framework versions
-
-- Transformers 4.33.0
-- Pytorch 2.0.0
-- Datasets 2.1.0
-- Tokenizers 0.13.3

wav2vec2-large-xls-r-300m-hi/added_tokens.json

@@ -1,4 +0,0 @@
-{
-  "</s>": 65,
-  "<s>": 64
-}

wav2vec2-large-xls-r-300m-hi/config.json

@@ -1,109 +0,0 @@
-{
-  "_name_or_path": "facebook/wav2vec2-xls-r-300m",
-  "activation_dropout": 0.0,
-  "adapter_attn_dim": null,
-  "adapter_kernel_size": 3,
-  "adapter_stride": 2,
-  "add_adapter": false,
-  "apply_spec_augment": true,
-  "architectures": [
-    "Wav2Vec2ForCTC"
-  ],
-  "attention_dropout": 0.1,
-  "bos_token_id": 1,
-  "classifier_proj_size": 256,
-  "codevector_dim": 768,
-  "contrastive_logits_temperature": 0.1,
-  "conv_bias": true,
-  "conv_dim": [
-    512,
-    512,
-    512,
-    512,
-    512,
-    512,
-    512
-  ],
-  "conv_kernel": [
-    10,
-    3,
-    3,
-    3,
-    3,
-    2,
-    2
-  ],
-  "conv_stride": [
-    5,
-    2,
-    2,
-    2,
-    2,
-    2,
-    2
-  ],
-  "ctc_loss_reduction": "mean",
-  "ctc_zero_infinity": false,
-  "diversity_loss_weight": 0.1,
-  "do_stable_layer_norm": true,
-  "eos_token_id": 2,
-  "feat_extract_activation": "gelu",
-  "feat_extract_dropout": 0.0,
-  "feat_extract_norm": "layer",
-  "feat_proj_dropout": 0.0,
-  "feat_quantizer_dropout": 0.0,
-  "final_dropout": 0.0,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout": 0.1,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-05,
-  "layerdrop": 0.1,
-  "mask_feature_length": 10,
-  "mask_feature_min_masks": 0,
-  "mask_feature_prob": 0.0,
-  "mask_time_length": 10,
-  "mask_time_min_masks": 2,
-  "mask_time_prob": 0.05,
-  "model_type": "wav2vec2",
-  "num_adapter_layers": 3,
-  "num_attention_heads": 16,
-  "num_codevector_groups": 2,
-  "num_codevectors_per_group": 320,
-  "num_conv_pos_embedding_groups": 16,
-  "num_conv_pos_embeddings": 128,
-  "num_feat_extract_layers": 7,
-  "num_hidden_layers": 24,
-  "num_negatives": 100,
-  "output_hidden_size": 1024,
-  "pad_token_id": 63,
-  "proj_codevector_dim": 768,
-  "tdnn_dilation": [
-    1,
-    2,
-    3,
-    1,
-    1
-  ],
-  "tdnn_dim": [
-    512,
-    512,
-    512,
-    512,
-    1500
-  ],
-  "tdnn_kernel": [
-    5,
-    3,
-    3,
-    1,
-    1
-  ],
-  "torch_dtype": "float32",
-  "transformers_version": "4.33.0",
-  "use_weighted_layer_sum": false,
-  "vocab_size": 66,
-  "xvector_output_dim": 512
-}

wav2vec2-large-xls-r-300m-hi/preprocessor_config.json

@@ -1,9 +0,0 @@
-{
-  "do_normalize": true,
-  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
-  "feature_size": 1,
-  "padding_side": "right",
-  "padding_value": 0.0,
-  "return_attention_mask": true,
-  "sampling_rate": 16000
-}

wav2vec2-large-xls-r-300m-hi/pytorch_model.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2c92db15bfca9b90057ebdc6f39c8842d05bfc9163720171b0e53c85c339b9ca
-size 1262172461

wav2vec2-large-xls-r-300m-hi/special_tokens_map.json

@@ -1,22 +0,0 @@
-{
-  "additional_special_tokens": [
-    {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false
-    },
-    {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": true,
-      "rstrip": false,
-      "single_word": false
-    }
-  ],
-  "bos_token": "<s>",
-  "eos_token": "</s>",
-  "pad_token": "[PAD]",
-  "unk_token": "[UNK]"
-}

wav2vec2-large-xls-r-300m-hi/tokenizer_config.json

@@ -1,13 +0,0 @@
-{
-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": true,
-  "do_lower_case": false,
-  "eos_token": "</s>",
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "[PAD]",
-  "replace_word_delimiter_char": " ",
-  "target_lang": null,
-  "tokenizer_class": "Wav2Vec2CTCTokenizer",
-  "unk_token": "[UNK]",
-  "word_delimiter_token": "|"
-}

wav2vec2-large-xls-r-300m-hi/training_args.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ba2c75a2ae554e1c46e400b58d8b91391c6ab8c243e2e90fb6e343b8b07b2436
-size 4027

wav2vec2-large-xls-r-300m-hi/vocab.json

@@ -1,66 +0,0 @@
-{
-  "[PAD]": 63,
-  "[UNK]": 62,
-  "|": 0,
-  "ँ": 1,
-  "ं": 2,
-  "ः": 3,
-  "अ": 4,
-  "आ": 5,
-  "इ": 6,
-  "ई": 7,
-  "उ": 8,
-  "ऊ": 9,
-  "ऋ": 10,
-  "ए": 11,
-  "ऐ": 12,
-  "ऑ": 13,
-  "ओ": 14,
-  "औ": 15,
-  "क": 16,
-  "ख": 17,
-  "ग": 18,
-  "घ": 19,
-  "च": 20,
-  "छ": 21,
-  "ज": 22,
-  "झ": 23,
-  "ञ": 24,
-  "ट": 25,
-  "ठ": 26,
-  "ड": 27,
-  "ढ": 28,
-  "ण": 29,
-  "त": 30,
-  "थ": 31,
-  "द": 32,
-  "ध": 33,
-  "न": 34,
-  "प": 35,
-  "फ": 36,
-  "ब": 37,
-  "भ": 38,
-  "म": 39,
-  "य": 40,
-  "र": 41,
-  "ल": 42,
-  "व": 43,
-  "श": 44,
-  "ष": 45,
-  "स": 46,
-  "ह": 47,
-  "़": 48,
-  "ा": 49,
-  "ि": 50,
-  "ी": 51,
-  "ु": 52,
-  "ू": 53,
-  "ृ": 54,
-  "े": 55,
-  "ै": 56,
-  "ॉ": 57,
-  "ो": 58,
-  "ौ": 59,
-  "्": 60,
-  "।": 61
-}