初始化项目，由ModelHub XC社区提供模型

Model: facebook/wav2vec2-base-10k-voxpopuli-ft-en
Source: Original Platform

README.md

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+---
+language: en
+tags:
+- audio
+- automatic-speech-recognition
+- voxpopuli
+license: cc-by-nc-4.0
+---
+
+# Wav2Vec2-Base-VoxPopuli-Finetuned
+
+[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained on the 10K unlabeled subset of [VoxPopuli corpus](https://arxiv.org/abs/2101.00390) and fine-tuned on the transcribed data in en (refer to Table 1 of paper for more information).
+
+**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
+Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
+
+**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*
+
+See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/)
+
+
+# Usage for inference
+
+In the following it is shown how the model can be used in inference on a sample of the [Common Voice dataset](https://commonvoice.mozilla.org/en/datasets)
+
+```python
+#!/usr/bin/env python3
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import torchaudio
+import torch
+
+# resample audio
+
+# load model & processor
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-10k-voxpopuli-ft-en")
+
+# load dataset
+ds = load_dataset("common_voice", "en", split="validation[:1%]")
+
+# common voice does not match target sampling rate
+common_voice_sample_rate = 48000
+target_sample_rate = 16000
+
+resampler = torchaudio.transforms.Resample(common_voice_sample_rate, target_sample_rate)
+
+
+# define mapping fn to read in sound file and resample
+def map_to_array(batch):
+    speech, _ = torchaudio.load(batch["path"])
+    speech = resampler(speech)
+    batch["speech"] = speech[0]
+    return batch
+
+
+# load all audio files
+ds = ds.map(map_to_array)
+
+# run inference on the first 5 data samples
+inputs = processor(ds[:5]["speech"], sampling_rate=target_sample_rate, return_tensors="pt", padding=True)
+
+# inference
+logits = model(**inputs).logits
+predicted_ids = torch.argmax(logits, axis=-1)
+
+print(processor.batch_decode(predicted_ids))
+```
+