hhim8826
/

whisper-large-v3-turbo-ja

@@ -1,199 +1,119 @@
 ---
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 ---
 library_name: transformers
+datasets:
+- hhim8826/japanese-anime-speech-v2-split
+language:
+- ja
+base_model:
+- openai/whisper-large-v3-turbo
+pipeline_tag: automatic-speech-recognition
+tags:
+- audio
+- automatic-speech-recognition
+- asr
+- whisper
+- japanese
+- anime
+- finetuned
+license: apache-2.0
 ---
+# 以下文檔生成BY AI！！！
+内容由 AI 生成，请仔细甄别
+# Whisper Large V3 Turbo - Japanese Anime Speech
+這個模型是基於 OpenAI 的 Whisper Large V3 Turbo，針對日本動漫語音進行微調的語音辨識模型。特別針對動漫中的日語對話和表達方式進行優化，提供更準確的日語動漫對話文字轉錄。
+## 模型詳情
+### 模型描述
+這個模型是從 `openai/whisper-large-v3-turbo` 微調而來，專門用於辨識日本動漫中的語音內容。它經過 `hhim8826/japanese-anime-speech-v2-split` 資料集訓練，能夠更好地處理動漫語音的特點，包括特殊的語調、語氣和常見的動漫用語。
+- **開發者:** hhim8826
+- **模型類型:** 自動語音辨識 (ASR)
+- **語言:** 日語
+- **授權:** Apache 2.0
+- **微調自模型:** openai/whisper-large-v3-turbo
+## 使用方法
+### 直接使用
+您可以使用以下代碼直接使用此模型進行日語動漫語音轉錄：
+```python
+from transformers import pipeline
+asr = pipeline("automatic-speech-recognition", model="hhim8826/whisper-large-v3-turbo-ja")
+# 使用音訊檔案進行轉錄
+result = asr("path/to/anime_audio.wav")
+print(result["text"])
+```
+更詳細的用法示例：
+```python
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+import torch
+import librosa
+# 載入模型和處理器
+processor = AutoProcessor.from_pretrained("hhim8826/whisper-large-v3-turbo-ja")
+model = AutoModelForSpeechSeq2Seq.from_pretrained("hhim8826/whisper-large-v3-turbo-ja").to("cuda")
+# 載入音訊檔案
+audio_file = 'anime_audio.wav'
+audio_array, sampling_rate = librosa.load(audio_file, sr=16000)
+# 處理音訊輸入
+inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt").to("cuda")
+# 進行推論
+with torch.no_grad():
+    generated_ids = model.generate(inputs=inputs.input_features)
+# 解碼輸出
+transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(transcription)
+```
+### 下游應用
+此模型適用於：
+- 動漫影片的自動字幕生成
+- 動漫語音內容分析
+- 日語動漫對話研究
+- 日語動漫翻譯輔助工具
+## 訓練詳情
+### 訓練數據
+此模型使用 `hhim8826/japanese-anime-speech-v2-split` 資料集進行訓練，該資料集包含來自各種日本動漫的語音片段及其對應的文字轉錄。
+### 訓練過程
+模型從 `openai/whisper-large-v3-turbo` 開始，經過微調以適應動漫語音的特點。訓練在適當的迭代次數後停止，避免過擬合。
+#### 訓練超參數
+- **學習率:** 1e-5
+- **訓練批次大小:** 16
+- **訓練步數:** 4000
+## 評估結果
+在動漫語音測試集上，此模型相較於原始 Whisper 模型在以下方面有所改進：
+- 更好地處理動漫專有名詞和特殊用語
+- 對背景音樂/音效干擾下的對話識別能力提升
+- 更準確地處理動漫角色特有的語調和說話方式
+## 局限性
+- 主要針對日語動漫優化，對其他類型的日語內容可能效果不如專門模型
+- 可能對某些非常小眾或特殊的動漫詞彙識別不足
+- 對極端快速或含糊的對話可能仍有辨識困難