Create README.md

README.md

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+---
+language: en
+datasets:
+- superb
+tags:
+- speech
+- audio
+- wav2vec2
+- audio-classification
+widget:
+- label: VoxCeleb Speaker id10003
+  src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
+- label: VoxCeleb Speaker id10004
+  src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
+license: apache-2.0
+---
+
+# Wav2Vec2-Base for Speaker Identification
+
+## Model description
+
+This is a ported version of 
+[S3PRL's Wav2Vec2 for the SUPERB Speaker Identification task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/voxceleb1).
+
+The base model is [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base), 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
+
+Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
+classification, where speakers are in the same predefined set for both training and testing. The widely
+used [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html) dataset is adopted
+
+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#sid-speaker-identification).
+
+
+## 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", "si", split="test")
+
+classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-sid")
+labels = classifier(dataset[0]["file"], top_k=5)
+```
+
+Or use the model directly:
+```python
+import torch
+import librosa
+from datasets import load_dataset
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
+
+def map_to_array(example):
+    speech, _ = librosa.load(example["file"], sr=16000, mono=True)
+    example["speech"] = speech
+    return example
+
+# load a demo dataset and read audio files
+dataset = load_dataset("anton-l/superb_demo", "si", split="test")
+dataset = dataset.map(map_to_array)
+
+model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid")
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid")
+
+# compute attention masks and normalize the waveform if needed
+inputs = feature_extractor(dataset[:2]["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**| `0.7518`  | `0.7518`         |
+
+### 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}
+}
+```