---
title: wav2vec2-conformer-rope-large-100h-ft
canonical_url: "https://www.modelscope.cn/models/facebook/wav2vec2-conformer-rope-large-100h-ft"
md_url: "https://www.modelscope.cn/models/facebook/wav2vec2-conformer-rope-large-100h-ft.md"
repository: facebook/wav2vec2-conformer-rope-large-100h-ft
last_updated: 2025-05-20
license: apache-2.0
model_type:
  - wav2vec2-conformer
architectures:
  - Wav2Vec2ConformerForCTC
library_name:
  - pytorch
  - transformer
frameworks:
  - pytorch
language:
  - en
downloads: 329
stars: 0
tags:
  - speech
  - audio
  - automatic-speech-recognition
  - hf-asr-leaderboard
---

# wav2vec2-conformer-rope-large-100h-ft

> wav2vec2-conformer-rope-large-100h-ft - facebook 在 ModelScope 开源的模型。Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings

facebook/wav2vec2-conformer-rope-large-100h-ft 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: facebook/wav2vec2-conformer-rope-large-100h-ft
- **License**: apache-2.0
- **Tags**: speech, audio, automatic-speech-recognition, hf-asr-leaderboard
- **Downloads**: 329
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/wav2vec2-conformer-rope-large-100h-ft

---

# Wav2Vec2-Conformer-Large-100h with Rotary Position Embeddings

Wav2Vec2 Conformer with rotary position embeddings, pretrained on 960h hours of Librispeech and fine-tuned on **100 hours of Librispeech** on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.

**Paper**: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)

**Authors**: Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino

The results of Wav2Vec2-Conformer can be found in Table 3 and Table 4 of the [official paper](https://arxiv.org/abs/2010.05171).

The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

# Usage

To transcribe audio files the model can be used as a standalone acoustic model as follows:

```python
 from transformers import Wav2Vec2Processor, Wav2Vec2ConformerForCTC
 from datasets import load_dataset
 import torch
 
 # load model and processor
 processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft")
 model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-100h-ft")
     
 # load dummy dataset and read soundfiles
 ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
 
 # tokenize
 input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values
 
 # retrieve logits
 logits = model(input_values).logits
 
 # take argmax and decode
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)
 ```