whisper-onnx-tensorrt

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whisper-onnx-tensorrt

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ONNX and TensorRT implementation of Whisper

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# whisper-onnx-tensorrt
ONNX and TensorRT implementation of Whisper.

This repository has been reimplemented with ONNX and TensorRT using [zhuzilin/whisper-openvino](https://github.com/zhuzilin/whisper-openvino) as a reference.

Enables execution only with onnxruntime with CUDA and TensorRT Excecution Provider enabled, no need to install PyTorch or TensorFlow. All backend logic using PyTorch was rewritten to a Numpy/CuPy implementation from scratch.

Click here for CPU version: https://github.com/PINTO0309/whisper-onnx-cpu

## 1. Environment
Although it can run directly on the host PC, I strongly recommend the use of Docker to avoid breaking the environment.

1. Docker
2. NVIDIA GPU (VRAM 16 GB or more recommended)
3. onnx 1.13.1
4. onnxruntime-gpu 1.13.1 (TensorRT Execution Provider custom)
5. CUDA 11.8
6. cuDNN 8.9
7. TensorRT 8.5.3
8. onnx-tensorrt 8.5-GA
9. cupy v12.0.0
10. etc (See Dockerfile.xxx)

## 2. Converted Models
https://github.com/PINTO0309/PINTO_model_zoo/tree/main/381_Whisper

## 3. Docker run
```bash
git clone https://github.com/PINTO0309/whisper-onnx-tensorrt.git && cd whisper-onnx-tensorrt
```
### 3-1. CUDA ver
```bash
docker run --rm -it --gpus all -v `pwd`:/workdir pinto0309/whisper-onnx-cuda
```
### 3-2. TensorRT ver
```bash
docker run --rm -it --gpus all -v `pwd`:/workdir pinto0309/whisper-onnx-tensorrt
```

## 4. Docker build
If you do not need to build the docker image by yourself, you do not need to perform this step.
### 4-1. CUDA ver
```bash
docker build -t whisper-onnx -f Dockerfile.gpu .
```
### 4-2. TensorRT ver
```bash
docker build -t whisper-onnx -f Dockerfile.tensorrt .
```
### 4-3. docker run
```bash
docker run --rm -it --gpus all -v `pwd`:/workdir whisper-onnx
```

## 5. Transcribe
- `--model` option
    ```
    tiny.en
    tiny
    base.en
    base
    small.en
    small
    medium.en
    medium
    large-v1
    large-v2
    ```
- command

The onnx file is automatically downloaded when the sample is run. Note that `Decoder` is run in CUDA, not TensorRT, because the shape of all input tensors must be undefined. When running the TensorRT version, there is a 5 to 10 minute wait for the compilation process from ONNX to the TensorRT Engine during the first inference. If `--language` is not specified, the tokenizer will auto-detect the language.
    ```bash
    python whisper/transcribe.py xxxx.mp4 --model small --beam_size 3
    ```
- results
    ```
    Detecting language using up to the first 30 seconds. Use `--language` to specify the language
    Detected language: Japanese
    [00:00.000 --> 00:07.200] ストレオシンの推定モデルの最適化 としまして 後半のパート2は 実際
    [00:07.200 --> 00:11.600] のデモを交えまして 普段私がどのように モデルを最適化して 様々な
    [00:11.600 --> 00:15.600] フレームワークの環境でプロイしてる かというのを実際に操作をこの
    [00:15.600 --> 00:18.280] 画面上で見ていただきながら ご理解いただけるように努めたい
    [00:18.280 --> 00:21.600] と思います それでは早速ですが こちらの
    [00:21.600 --> 00:26.320] GitHubの方に本日の公演内容について は すべてチュートリアルをまとめて
    [00:26.320 --> 00:31.680] コミットしております 2021.0.20.28 インテルティブラーニング
    [00:31.680 --> 00:35.200] でヒットネットデモという ちょっと長い名前なんですけれども 現状
    [00:35.200 --> 00:39.120] はプライベートになってますが この公演のタイミングでパブリック
    [00:39.120 --> 00:43.440] の方に変更したいと思っております 基本的にはこちらの上から順前
    [00:43.440 --> 00:48.000] ですね チュートリアルを謎って いくという形になります
    [00:48.000 --> 00:52.640] まず本日対象にするモデルの内容 なんですけれども Google Research
    ```
- parameters
    ```
    usage: transcribe.py
        [-h]
        [--model {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2}]
        [--output_dir OUTPUT_DIR]
        [--verbose VERBOSE]
        [--disable_cupy]
        [--task {transcribe,translate}]
        [--language {af, am, ...}]
        [--temperature TEMPERATURE]
        [--best_of BEST_OF]
        [--beam_size BEAM_SIZE]
        [--patience PATIENCE]
        [--length_penalty LENGTH_PENALTY]
        [--suppress_tokens SUPPRESS_TOKENS]
        [--initial_prompt INITIAL_PROMPT]
        [--condition_on_previous_text CONDITION_ON_PREVIOUS_TEXT]
        [--temperature_increment_on_fallback TEMPERATURE_INCREMENT_ON_FALLBACK]
        [--compression_ratio_threshold COMPRESSION_RATIO_THRESHOLD]
        [--logprob_threshold LOGPROB_THRESHOLD]
        [--no_speech_threshold NO_SPEECH_THRESHOLD]
        audio [audio ...]

positional arguments:
      audio
        audio file(s) to transcribe

optional arguments:
      -h, --help
        show this help message and exit
      --model {tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2}
        name of the Whisper model to use
        (default: small)
      --output_dir OUTPUT_DIR, -o OUTPUT_DIR
        directory to save the outputs
        (default: .)
      --verbose VERBOSE
        whether to print out the progress and debug messages
        (default: True)
      --disable_cupy
        When Out of Memory occurs due to insufficient GPU RAM, this option suppresses GPU
        RAM consumption.
      --task {transcribe,translate}
        whether to perform X->X speech recognition ('transcribe') or
        X->English translation ('translate')
        (default: transcribe)
      --language {af, am, ...}
        language spoken in the audio, specify None to perform language detection
        (default: None)
      --temperature TEMPERATURE
        temperature to use for sampling
        (default: 0)
      --best_of BEST_OF
        number of candidates when sampling with non-zero temperature
        (default: 5)
      --beam_size BEAM_SIZE
        number of beams in beam search, only applicable when temperature is zero
        (default: 5)
      --patience PATIENCE
        optional patience value to use in beam decoding,
        as in https://arxiv.org/abs/2204.05424,
        the default (1.0) is equivalent to conventional beam search
        (default: None)
      --length_penalty LENGTH_PENALTY
        optional token length penalty coefficient (alpha) as in
        https://arxiv.org/abs/1609.08144, uses simple lengt normalization by default
        (default: None)
      --suppress_tokens SUPPRESS_TOKENS
        comma-separated list of token ids to suppress during sampling;
        '-1' will suppress most special characters except common punctuations
        (default: -1)
      --initial_prompt INITIAL_PROMPT
        optional text to provide as a prompt for the first window.
        (default: None)
      --condition_on_previous_text CONDITION_ON_PREVIOUS_TEXT
        if True, provide the previous output of the model as a prompt for the next window;
        disabling may make the text inconsistent across windows, but the model becomes
        less prone to getting stuck in a failure loop
        (default: True)
      --temperature_increment_on_fallback TEMPERATURE_INCREMENT_ON_FALLBACK
        temperature to increase when falling back when the decoding fails to meet either of
        the thresholds below
        (default: 0.2)
      --compression_ratio_threshold COMPRESSION_RATIO_THRESHOLD
        if the gzip compression ratio is higher than this value, treat the decoding as failed
        (default: 2.4)
      --logprob_threshold LOGPROB_THRESHOLD
        if the average log probability is lower than this value, treat the decoding as failed
        (default: -1.0)
      --no_speech_threshold NO_SPEECH_THRESHOLD
        if the probability of the <|nospeech|> token is higher than this value AND
        the decoding has failed due to `logprob_threshold`, consider the segment as silence
        (default: 0.6)
    ```
## 6. Languages
https://github.com/PINTO0309/whisper-onnx-tensorrt/blob/main/whisper/tokenizer.py
```
LANGUAGES = {
    "en": "english",
    "zh": "chinese",
    "de": "german",
    "es": "spanish",
    "ru": "russian",
    "ko": "korean",
    "fr": "french",
    "ja": "japanese",
    "pt": "portuguese",
    "tr": "turkish",
    "pl": "polish",
    "ca": "catalan",
    "nl": "dutch",
    "ar": "arabic",
    "sv": "swedish",
    "it": "italian",
    "id": "indonesian",
    "hi": "hindi",
    "fi": "finnish",
    "vi": "vietnamese",
    "iw": "hebrew",
    "uk": "ukrainian",
    "el": "greek",
    "ms": "malay",
    "cs": "czech",
    "ro": "romanian",
    "da": "danish",
    "hu": "hungarian",
    "ta": "tamil",
    "no": "norwegian",
    "th": "thai",
    "ur": "urdu",
    "hr": "croatian",
    "bg": "bulgarian",
    "lt": "lithuanian",
    "la": "latin",
    "mi": "maori",
    "ml": "malayalam",
    "cy": "welsh",
    "sk": "slovak",
    "te": "telugu",
    "fa": "persian",
    "lv": "latvian",
    "bn": "bengali",
    "sr": "serbian",
    "az": "azerbaijani",
    "sl": "slovenian",
    "kn": "kannada",
    "et": "estonian",
    "mk": "macedonian",
    "br": "breton",
    "eu": "basque",
    "is": "icelandic",
    "hy": "armenian",
    "ne": "nepali",
    "mn": "mongolian",
    "bs": "bosnian",
    "kk": "kazakh",
    "sq": "albanian",
    "sw": "swahili",
    "gl": "galician",
    "mr": "marathi",
    "pa": "punjabi",
    "si": "sinhala",
    "km": "khmer",
    "sn": "shona",
    "yo": "yoruba",
    "so": "somali",
    "af": "afrikaans",
    "oc": "occitan",
    "ka": "georgian",
    "be": "belarusian",
    "tg": "tajik",
    "sd": "sindhi",
    "gu": "gujarati",
    "am": "amharic",
    "yi": "yiddish",
    "lo": "lao",
    "uz": "uzbek",
    "fo": "faroese",
    "ht": "haitian creole",
    "ps": "pashto",
    "tk": "turkmen",
    "nn": "nynorsk",
    "mt": "maltese",
    "sa": "sanskrit",
    "lb": "luxembourgish",
    "my": "myanmar",
    "bo": "tibetan",
    "tl": "tagalog",
    "mg": "malagasy",
    "as": "assamese",
    "tt": "tatar",
    "haw": "hawaiian",
    "ln": "lingala",
    "ha": "hausa",
    "ba": "bashkir",
    "jw": "javanese",
    "su": "sundanese",
}
```

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