constants.py

from textwrap import dedent

BANNER_TEXT = """
<div style="text-align: center;">
    <h1><a href='https://github.com/argmaxinc/WhisperKitAndroid'>WhisperKit Android Benchmarks</a></h1>
</div>
"""


INTRO_LABEL = """We present comprehensive benchmarks for WhisperKit Android, our on-device ASR solution for Android devices, compared against a reference implementation. These benchmarks aim to help developers and enterprises make informed decisions when choosing optimized or compressed variants of machine learning models for production use. Show more."""


INTRO_TEXT = """
<h3 style="display: flex;
  justify-content: center;
  align-items: center;
"></h2>
\n📈 Key Metrics:  
Word Error Rate (WER) (⬇️): The percentage of words incorrectly transcribed. Lower is better.  
Quality of Inference (QoI) (⬆️): Percentage of examples where WhisperKit Android performs no worse than the reference model. Higher is better.  
Tokens per Second (⬆️): The number of output tokens generated per second. Higher is better.  
Speed (⬆️): Input audio seconds transcribed per second. Higher is better.
🎯 WhisperKi Android is evaluated across different datasets, with a focus on per-example no-regressions (QoI) and overall accuracy (WER).
\n💻 Our benchmarks include:  
Reference: <a href='https://platform.openai.com/docs/guides/speech-to-text'>WhisperOpenAIAPI</a> (OpenAI's Whisper API)  
On-device: <a href='https://github.com/argmaxinc/WhisperKitAndroid'>WhisperKit Android</a> (various versions and optimizations)  
ℹ️ Reference Implementation:  
<a href='https://platform.openai.com/docs/guides/speech-to-text'>WhisperOpenAIAPI</a> sets the reference standard. We assume it uses the equivalent of openai/whisper-large-v2 in float16 precision, along with additional undisclosed optimizations from OpenAI. As of 02/29/24, it costs $0.36 per hour of audio and has a 25MB file size limit per request.
\n🔍 We use two primary datasets:  
<a href='https://huggingface.co/datasets/argmaxinc/librispeech'>LibriSpeech</a>: ~5 hours of short English audio clips  
<a href='https://huggingface.co/datasets/argmaxinc/earnings22'>Earnings22</a>: ~120 hours of English audio from earnings calls  
🔄 Results are periodically updated using our automated evaluation pipeline on Apple Silicon Macs.
\n🛠️ Developers can use <a href='https://github.com/argmaxinc/WhisperKitAndroid'>WhisperKit Android</a> to reproduce these results or run evaluations on their own custom datasets.
🔗 Links:
- <a href='https://github.com/argmaxinc/WhisperKit Android'>WhisperKit Android</a>
- <a href='https://github.com/argmaxinc/whisperkittools'>whisperkittools</a>
- <a href='https://huggingface.co/datasets/argmaxinc/librispeech'>LibriSpeech</a>
- <a href='https://huggingface.co/datasets/argmaxinc/earnings22'>Earnings22</a>
- <a href='https://platform.openai.com/docs/guides/speech-to-text'>WhisperOpenAIAPI</a>
"""


METHODOLOGY_TEXT = dedent(
    """
    # Methodology
    ## Overview
    WhisperKit Android Benchmarks is the one-stop shop for on-device performance and quality testing of WhisperKit Android models across supported devices, OS versions and audio datasets.
    ## Metrics
    - **Speed factor** (⬆️): Computed as the ratio of input audio length to end-to-end WhisperKit Android latency for transcribing that audio. A speed factor of N means N seconds of input audio was transcribed in 1 second.
    - **Tok/s (Tokens per second)** (⬆️): Total number of text decoder forward passes divided by the end-to-end processing time.
        - This metric varies with input data given that the pace of speech changes the text decoder % of overall latency. This metric should not be confused with the reciprocal of the text decoder latency which is constant across input files.
    - **WER (Word Error Rate)** (⬇️): The ratio of words incorrectly transcribed when comparing the model's output to reference transcriptions, with lower values indicating better accuracy.
    - **QoI (Quality of Inference)** (⬆️): The ratio of examples where WhisperKit Android performs no worse than the reference model.
        - This metric does not capture improvements to the reference. It only measures potential regressions.
    
    ## Data
    - **Short-form**: 10 minutes of English audiobook clips with 30s/clip comprising a subset of the [librispeech test set](https://huggingface.co/datasets/argmaxinc/librispeech). Proxy for average streaming performance.
    - **Long-form**: 10 minutes of earnings call recordings in English. Built from the [earnings22 test set](https://huggingface.co/datasets/argmaxinc/earnings22-12hours). Proxy for average from-file performance.
    - Full datasets are used for English Quality tests and random 10-minute subsets are used for Performance tests.
    ## Performance Measurement
    1. On-device testing is conducted with [WhisperKit Android Tests](https://github.com/argmaxinc/WhisperKitAndroid) on Android devices, across different Android versions.
    2. Performance is recorded on 10-minute datasets described above for short- and long-form
    3. Quality metrics are recorded on 10-minute datasets using an Apple M2 Pro CPU on a Linux host to allow for fast processing of many configurations and providing a consistent, high-performance baseline for all evaluations displayed in the English Quality tab.
    4. Results are aggregated and presented in the dashboard, allowing for easy comparison and analysis.
    ## Dashboard Features
    - Performance: Interactive filtering by model, device, OS, and performance metrics
    - Timeline: Visualizations of performance trends
    - English Quality: English transcription quality on short- and long-form audio
	- Device Support: Matrix of supported device, OS and model version combinations. Unsupported combinations are marked with :warning:.
    - This methodology ensures a comprehensive and fair evaluation of speech recognition models supported by WhisperKit Android across a wide range of scenarios and use cases.
"""
)

PERFORMANCE_TEXT = dedent(
    """
    ## Metrics
    - **Speed factor** (⬆️): Computed as the ratio of input audio length to end-to-end WhisperKit Android latency for transcribing that audio. A speed factor of N means N seconds of input audio was transcribed in 1 second.
    - **Tok/s (Tokens per second)** (⬆️): Total number of text decoder forward passes divided by the end-to-end processing time.
    ## Data
   - **Short-form**: 10 minutes of English audiobook clips with 30s/clip comprising the [librispeech test set](https://huggingface.co/datasets/argmaxinc/librispeech).
    - **Long-form**: 10 minutes of earnings call recordings in English with various accents. Built from the [earnings22 test set](https://huggingface.co/datasets/argmaxinc/earnings22-12hours).
"""
)

QUALITY_TEXT = dedent(
    """
    ## Metrics
    - **WER (Word Error Rate)** (⬇️): The ratio of words incorrectly transcribed when comparing the model's output to reference transcriptions, with lower values indicating better accuracy.
    - **QoI (Quality of Inference)** (⬆️): The ratio of examples where WhisperKit Android performs no worse than the reference model.
        - This metric does not capture improvements to the reference. It only measures potential regressions.
"""
)

COL_NAMES = {
    "model.model_version": "Model",
    "device.product_name": "Device",
    "device.os": "OS",
    "average_wer": "Average WER",
    "qoi": "QoI",
    "speed": "Speed",
    "tokens_per_second": "Tok / s",
    "model": "Model",
    "device": "Device",
    "os": "OS",
    "english_wer": "English WER",
    "multilingual_wer": "Multilingual WER",
}


CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"


CITATION_BUTTON_TEXT = r"""@misc{whisperkit-android-argmax,
   title = {WhisperKit Android},
   author = {Argmax, Inc.},
   year = {2024},
   URL = {https://github.com/argmaxinc/WhisperKitAndroid}
}"""


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"
            style="display:block;width:7%;height:auto;"
        />
        </div>
</div>"""


EARNINGS22_URL = (
    "https://huggingface.co/datasets/argmaxinc/earnings22-debug/resolve/main/{0}"
)
LIBRISPEECH_URL = (
    "https://huggingface.co/datasets/argmaxinc/librispeech-debug/resolve/main/{0}"
)

AUDIO_URL = (
    "https://huggingface.co/datasets/argmaxinc/whisperkit-test-data/resolve/main/"
)

WHISPER_OPEN_AI_LINK = "https://huggingface.co/datasets/argmaxinc/whisperkit-evals/tree/main/WhisperKit/{}/{}"

BASE_WHISPERKIT_BENCHMARK_URL = "https://huggingface.co/datasets/argmaxinc/whisperkit-evals-dataset/blob/main/benchmark_data"