Training in progress, step 800

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README.md

@@ -1,199 +1,96 @@
 ---
-library_name: transformers
-tags: []
+language:
+- ne
+license: apache-2.0
+tags:
+- generated_from_trainer
+- automatic-speech-recognition
+- speech
+- openslr
+- nepali
+datasets:
+- spktsagar/openslr-nepali-asr-cleaned
+metrics:
+- wer
+base_model: facebook/wav2vec2-xls-r-300m
+model-index:
+- name: wav2vec2-large-xls-r-300m-nepali-openslr
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Nepali Speech Recognition
+    dataset:
+      name: OpenSLR Nepali ASR
+      type: spktsagar/openslr-nepali-asr-cleaned
+      config: original
+      split: train
+    metrics:
+    - type: were
+      value: 21.27
+      name: Test WER
+      verified: false
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-
-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-## Bias, Risks, and Limitations
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-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
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-
-## Technical Specifications [optional]
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-## Citation [optional]
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-## Glossary [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-[More Information Needed]
+<!-- 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-nepali-openslr
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an [OpenSLR Nepali ASR](https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned) dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.1767
+- eval_wer: 0.2127
+- eval_runtime: 595.3962
+- eval_samples_per_second: 36.273
+- eval_steps_per_second: 4.535
+- epoch: 6.07
+- step: 23200
+
+## Model description
+
+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 one of the most popular English datasets for ASR, called LibriSpeech, Facebook AI presented a multi-lingual version of Wav2Vec2, called XLSR. XLSR stands for cross-lingual speech representations and refers to model's ability to learn speech representations that are useful across multiple languages.
+
+## How to use?
+1. Install transformers and librosa
+```
+pip install librosa, transformers
+```
+2. Run the following code which loads your audio file, preprocessor, models, and returns your prediction
+```python
+import librosa
+from transformers import pipeline
+
+audio, sample_rate = librosa.load("<path to your audio file>", sr=16000)
+recognizer = pipeline("automatic-speech-recognition", model="spktsagar/wav2vec2-large-xls-r-300m-nepali-openslr")
+prediction = recognizer(audio)
+```
+
+## Intended uses & limitations
+
+The model is trained on the OpenSLR Nepali ASR dataset, which in itself has some incorrect transcriptions, so it is obvious that the model will not have perfect predictions for your transcript. Similarly, due to colab's resource limit utterances longer than 5 sec are filtered out from the dataset during training and evaluation. Hence, the model might not perform as expected when given audio input longer than 5 sec.
+
+## Training and evaluation data and Training procedure
+
+For dataset preparation and training code, please consult [my blog](https://sagar-spkt.github.io/posts/2022/08/finetune-xlsr-nepali/).
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.11.0+cu113
+- Datasets 2.6.0
+- Tokenizers 0.13.1

config.json

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+{
+  "_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": [
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+    512,
+    512,
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+    512,
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+  ],
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+  "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,
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+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.075,
+  "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": 0,
+  "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": "4.48.3",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 81,
+  "xvector_output_dim": 512
+}

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