Wav2vec2 2 Bart Large Tedlium

🚀 Wav2Vec2-2-Bart-Large-Tedlium

本模型是一個序列到序列（seq2seq）模型，在 TEDLIUM 語料庫（第3版）上進行訓練。它將語音編碼器與文本解碼器相結合，以執行自動語音識別任務。編碼器的權重使用來自 @facebook 的 Wav2Vec2 LV-60k 檢查點進行初始化。解碼器的權重使用來自 @facebook 的 Bart large 檢查點進行初始化。

使用該模型時，請確保您的語音輸入採樣率為 16Khz。該模型在開發集上的詞錯誤率（WER）為 9.0%，在測試集上為 6.4%。訓練日誌 記錄了 50k 步微調過程中的訓練和評估進度。

🚀 快速開始

本模型可作為獨立的聲學模型用於轉錄音頻文件，使用方法如下：

💻 使用示例

基礎用法

 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])

📚 詳細文檔

評估

以下代碼片段展示瞭如何在 TEDLIUM 測試數據上評估 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"]))

📄 許可證

本項目採用 CC BY 4.0 許可證。

📊 模型指標

⚠️ 重要提示

使用該模型時，請確保您的語音輸入採樣率為 16Khz。