README.md

@@ -1,100 +0,0 @@
----
-language: en
-datasets:
-- superb
-tags:
-- speech
-- audio
-- hubert
-- audio-classification
-license: apache-2.0
-widget:
-- example_title: Speech Commands "down"
-  src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
-- example_title: Speech Commands "go"
-  src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_go.wav
----
-
-# Hubert-Base for Keyword Spotting
-
-## Model description
-
-This is a ported version of [S3PRL's Hubert for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands).
-
-The base model is [hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960), which is pretrained on 16kHz 
-sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 
-
-For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051)
-
-## Task and dataset description
-
-Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of 
-words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
-inference time are all crucial. SUPERB uses the widely used 
-[Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task.
-The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
-false positive. 
-
-For the original model's training and evaluation instructions refer to the 
-[S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#ks-keyword-spotting).
-
-
-## Usage examples
-
-You can use the model via the Audio Classification pipeline:
-```python
-from datasets import load_dataset
-from transformers import pipeline
-
-dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
-
-classifier = pipeline("audio-classification", model="superb/hubert-base-superb-ks")
-labels = classifier(dataset[0]["file"], top_k=5)
-```
-
-Or use the model directly:
-```python
-import torch
-from datasets import load_dataset
-from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor
-from torchaudio.sox_effects import apply_effects_file
-
-effects = [["channels", "1"], ["rate", "16000"], ["gain", "-3.0"]]
-def map_to_array(example):
-    speech, _ = apply_effects_file(example["file"], effects)
-    example["speech"] = speech.squeeze(0).numpy()
-    return example
-
-# load a demo dataset and read audio files
-dataset = load_dataset("anton-l/superb_demo", "ks", split="test")
-dataset = dataset.map(map_to_array)
-
-model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ks")
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
-
-# compute attention masks and normalize the waveform if needed
-inputs = feature_extractor(dataset[:4]["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
-
-logits = model(**inputs).logits
-predicted_ids = torch.argmax(logits, dim=-1)
-labels = [model.config.id2label[_id] for _id in predicted_ids.tolist()]
-```
-
-## Eval results
-
-The evaluation metric is accuracy.
-
-|        | **s3prl** | **transformers** |
-|--------|-----------|------------------|
-|**test**| `0.9630`  | `0.9672`         |
-
-### BibTeX entry and citation info
-
-```bibtex
-@article{yang2021superb,
-  title={SUPERB: Speech processing Universal PERformance Benchmark},
-  author={Yang, Shu-wen and Chi, Po-Han and Chuang, Yung-Sung and Lai, Cheng-I Jeff and Lakhotia, Kushal and Lin, Yist Y and Liu, Andy T and Shi, Jiatong and Chang, Xuankai and Lin, Guan-Ting and others},
-  journal={arXiv preprint arXiv:2105.01051},
-  year={2021}
-}
-```

config.json

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-{
-  "_name_or_path": "facebook/hubert-base-ls960",
-  "activation_dropout": 0.1,
-  "apply_spec_augment": true,
-  "architectures": [
-    "HubertForSequenceClassification"
-  ],
-  "attention_dropout": 0.1,
-  "bos_token_id": 1,
-  "classifier_proj_size": 256,
-  "conv_bias": false,
-  "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": "sum",
-  "ctc_zero_infinity": false,
-  "do_stable_layer_norm": false,
-  "eos_token_id": 2,
-  "feat_extract_activation": "gelu",
-  "feat_extract_dropout": 0.0,
-  "feat_extract_norm": "group",
-  "feat_proj_dropout": 0.1,
-  "final_dropout": 0.1,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout": 0.1,
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "id2label": {
-    "0": "yes",
-    "1": "no",
-    "2": "up",
-    "3": "down",
-    "4": "left",
-    "5": "right",
-    "6": "on",
-    "7": "off",
-    "8": "stop",
-    "9": "go",
-    "10": "_unknown_",
-    "11": "_silence_"
-  },
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "label2id": {
-    "_silence_": 11,
-    "_unknown_": 10,
-    "down": 3,
-    "go": 9,
-    "left": 4,
-    "no": 1,
-    "off": 7,
-    "on": 6,
-    "right": 5,
-    "stop": 8,
-    "up": 2,
-    "yes": 0
-  },
-  "layer_norm_eps": 1e-05,
-  "layerdrop": 0.1,
-  "mask_feature_length": 10,
-  "mask_feature_prob": 0.0,
-  "mask_time_length": 10,
-  "mask_time_prob": 0.05,
-  "model_type": "hubert",
-  "num_attention_heads": 12,
-  "num_conv_pos_embedding_groups": 16,
-  "num_conv_pos_embeddings": 128,
-  "num_feat_extract_layers": 7,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "problem_type": "single_label_classification",
-  "torch_dtype": "float32",
-  "transformers_version": "4.10.0.dev0",
-  "use_weighted_layer_sum": true,
-  "vocab_size": 32
-}

preprocessor_config.json

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