Wav2vec2 2 Bart Large Tedlium

🚀 Wav2Vec2-2-Bart-Large-Tedlium

本模型是一个序列到序列（seq2seq）模型，在 TEDLIUM 语料库（第3版）上进行训练。它将语音编码器与文本解码器相结合，以执行自动语音识别任务。编码器的权重使用来自 @facebook 的 Wav2Vec2 LV-60k 检查点进行初始化。解码器的权重使用来自 @facebook 的 Bart large 检查点进行初始化。

使用该模型时，请确保您的语音输入采样率为 16Khz。该模型在开发集上的词错误率（WER）为 9.0%，在测试集上为 6.4%。训练日志 记录了 50k 步微调过程中的训练和评估进度。

🚀 快速开始

本模型可作为独立的声学模型用于转录音频文件，使用方法如下：

💻 使用示例

基础用法

 from transformers import AutoProcessor, SpeechEncoderDecoderModel
 from datasets import load_dataset
 import torch
 
 # load model and processor
 processor = AutoProcessor.from_pretrained("sanchit-gandhi/wav2vec2-2-bart-large-tedlium")
 model = SpeechEncoderDecoderModel.from_pretrained("sanchit-gandhi/wav2vec2-2-bart-large-tedlium")
     
 # load dummy dataset
 ds = load_dataset("sanchit-gandhi/tedlium_dummy", split="validation")
 
 # process audio inputs
 input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1
 
 # run inference (greedy search)
 generated = model.generate(input_values)
 
 # decode
 decoded = processor.batch_decode(generated, skip_special_tokens=True)
 print("Target: ", ds["text"][0])
 print("Transcription: ", decoded[0])

📚 详细文档

评估

以下代码片段展示了如何在 TEDLIUM 测试数据上评估 Wav2Vec2-Large-Tedlium 模型：

from datasets import load_dataset
from transformers import AutoProcessor, SpeechEncoderDecoderModel
import torch
from jiwer import wer

tedlium_eval = load_dataset("LIUM/tedlium", "release3", split="test")

def filter_ds(text):
    return text != "ignore_time_segment_in_scoring"

# remove samples ignored from scoring
tedlium_eval = tedlium_eval.map(filter_ds, input_columns=["text"])

model = SpeechEncoderDecoderModel.from_pretrained("sanchit-gandhi/wav2vec2-2-bart-large-tedlium").to("cuda")
processor = AutoProcessor.from_pretrained("sanchit-gandhi/wav2vec2-2-bart-large-tedlium")

gen_kwargs = {
        "max_length": 200,
        "num_beams": 5,
        "length_penalty": 1.2
        }

def map_to_pred(batch):
    input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
    with torch.no_grad():
        generated = model.generate(input_values.to("cuda"), **gen_kwargs)
    decoded = processor.batch_decode(generated, skip_special_tokens=True)
    batch["transcription"] = decoded[0]
    return batch

result = tedlium_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))

📄 许可证

本项目采用 CC BY 4.0 许可证。

📊 模型指标

⚠️ 重要提示

使用该模型时，请确保您的语音输入采样率为 16Khz。