---
title: OpenAI Whisper Vs Alibaba SenseVoice Small
emoji: ⚡
colorFrom: gray
colorTo: purple
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
license: mit
short_description: Compare OpenAI Whisper against FunAudioLLM SenseVoice.
---

# OpenAI Whisper vs. Alibaba SenseVoice Comparison

This Space lets you compare **faster-whisper** models against Alibaba FunAudioLLM’s **SenseVoice** models for automatic speech recognition (ASR), featuring:

* Multiple faster-whisper and SenseVoice model choices.
* Language selection for each ASR engine (full list of language codes).
* Explicit device selection (GPU or CPU) with ZeroGPU support (`spaces.GPU` decorator).
* Speaker diarization with `pyannote.audio`, displaying speaker-labeled transcripts.
* Simplified Chinese to Traditional Chinese conversion via `opencc`.
* Color-coded and scrollable diarized transcript panel.
* Semi‑streaming output: incremental transcript updates accumulate live as each segment or speaker turn completes.
* Semi‑real‑time diarized transcription: speaker‑labeled segments appear incrementally as they finish processing.

## 🚀 How to Use

1. Upload an audio file or record from your microphone.
2. **Faster-Whisper ASR**:

   1. Select a model variant from the dropdown.
   2. Choose the transcription language (default: auto-detect).
   3. Pick device: GPU or CPU.
   4. Toggle diarization on/off.
   5. Click **Transcribe with Faster-Whisper**.
3. **SenseVoice ASR**:

   1. Select a SenseVoice model.
   2. Choose the transcription language.
   3. Pick device: GPU or CPU.
   4. Toggle punctuation on/off.
   5. Toggle diarization on/off.
   6. Click **Transcribe with SenseVoice**.
4. View both the plain transcript and the color-coded, speaker-labeled diarized transcript side by side.

## 📁 Files

* **app.py**
  Main Gradio app implementing dual ASR pipelines with device control, diarization, and Chinese conversion.
* **requirements.txt**
  Python dependencies: Gradio, PyTorch, Transformers, faster-whisper, funasr, pyannote.audio, pydub, opencc-python-reimplemented, ctranslate2, termcolor, NVIDIA cuBLAS/cuDNN wheels.
* **Dockerfile** (optional)
  Defines a CUDA 12 + cuDNN 9 environment for GPU acceleration.

## ⚠️ Notes

* **Hugging Face token**: Set `HF_TOKEN` (or `HUGGINGFACE_TOKEN`) in Space secrets for authenticated diarization model access.
* **GPU allocation**: GPU resources are acquired only when GPU is explicitly selected, thanks to the `spaces.GPU` decorator.
* **Python version**: Python 3.10+ recommended.
* **System `ffmpeg`**: Ensure `ffmpeg` is installed on the host (or via Dockerfile) for audio processing.

## 🛠️ Dependencies

* **Python**: 3.10+
* **gradio** (>=3.39.0)
* **torch** (>=2.0.0) & **torchaudio**
* **transformers** (>=4.35.0)
* **faster-whisper** (>=1.1.1) & **ctranslate2** (==4.5.0)
* **funasr** (>=1.0.14)
* **pyannote.audio** (>=2.1.1) & **huggingface-hub** (>=0.18.0)
* **pydub** (>=0.25.1) & **ffmpeg-python** (>=0.2.0)
* **opencc-python-reimplemented**
* **termcolor**
* **nvidia-cublas-cu12**, **nvidia-cudnn-cu12**

## License

MIT

---

## 中文（臺灣）版本

# OpenAI Whisper vs. Alibaba FunASR SenseVoice 功能說明

本 Space 同步比較 **faster-whisper** 與 Alibaba FunAudioLLM 的 **SenseVoice** 模型，提供以下特色：

* 多款 faster-whisper 與 SenseVoice 模型可自由選擇
* 支援設定辨識語言（完整語言代碼列表）
* 明確切換運算裝置 (GPU/CPU)，並以 `spaces.GPU` 裝飾器延後 GPU 資源配置
* 整合 `pyannote.audio` 做語者分離，並在抄本中標示不同語者
* 使用 `opencc` 自動將簡體中文轉為臺灣繁體中文
* 彩色區隔對話式抄本，可捲動瀏覽及複製
* 半即時分段輸出：每段語音或語者片段處理完成後，即時累積顯示抄本

## 🚀 使用步驟

1. 上傳音檔或透過麥克風錄製音訊。
2. **Faster-Whisper ASR**：

   1. 選擇模型版本。
   2. 選定辨識語言 (預設自動偵測)。
   3. 切換運算裝置：GPU 或 CPU。
   4. 開啟/關閉語者分離功能。
   5. 點擊「Transcribe with Faster-Whisper」。
3. **SenseVoice ASR**：

   1. 選擇 SenseVoice 模型。
   2. 設定辨識語言。
   3. 切換運算裝置：GPU 或 CPU。
   4. 開啟/關閉標點符號功能。
   5. 開啟/關閉語者分離功能。
   6. 點擊「Transcribe with SenseVoice」。
4. 左右並排查看純文字抄本與彩色標註的語者分離抄本。

## 📁 檔案結構

* **app.py**
  Gradio 應用程式原始碼，實作雙 ASR 流程，包含運算裝置選擇、語者分離與中文轉換。
* **requirements.txt**
  Python 相依套件：Gradio、PyTorch、Transformers、faster-whisper、funasr、pyannote.audio、pydub、opencc-python-reimplemented、ctranslate2、termcolor、cuBLAS/cuDNN。
* **Dockerfile**（選用）
  定義 CUDA 12 + cuDNN 9 的 Docker 環境。

## ⚠️ 注意事項

* **Hugging Face 權杖**：請在 Space Secrets 設定 `HF_TOKEN` 或 `HUGGINGFACE_TOKEN`，以便下載語者分離模型。
* **GPU 分配**：僅於選擇 GPU 時才會申請 GPU 資源。
* **Python 版本**：建議使用 Python 3.10 以上。
* **系統 ffmpeg**：請確保主機或容器中已安裝 ffmpeg，以支援音訊處理。

## 🛠️ 相依套件

* **Python**: 3.10+
* **gradio**: >=3.39.0
* **torch** & **torchaudio**: >=2.0.0
* **transformers**: >=4.35.0
* **faster-whisper**: >=1.1.1 & **ctranslate2**: ==4.5.0
* **funasr**: >=1.0.14
* **pyannote.audio**: >=2.1.1 & **huggingface-hub**: >=0.18.0
* **pydub**: >=0.25.1 & **ffmpeg-python**: >=0.2.0
* **opencc-python-reimplemented**
* **termcolor**
* **nvidia-cublas-cu12**, **nvidia-cudnn-cu12**

## 授權

MIT