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| from pathlib import Path | |
| # Directory where request by models are stored | |
| DIR_OUTPUT_REQUESTS = Path("requested_models") | |
| EVAL_REQUESTS_PATH = Path("eval_requests") | |
| ########################## | |
| # Text definitions # | |
| ########################## | |
| banner_url = "https://huggingface.co/datasets/reach-vb/random-images/resolve/main/asr_leaderboard.png" | |
| BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 300px; max-width: 600px;"> </div>' | |
| TITLE = "<html> <head> <style> h1 {text-align: center;} </style> </head> <body> <h1> 🤗 Open Automatic Speech Recognition Leaderboard </b> </body> </html>" | |
| INTRODUCTION_TEXT = "📐 The 🤗 Open ASR Leaderboard ranks and evaluates speech recognition models \ | |
| on the Hugging Face Hub. \ | |
| \nWe report the Average [WER](https://huggingface.co/spaces/evaluate-metric/wer) (⬇️) and [RTF](https://openvoice-tech.net/index.php/Real-time-factor) (⬇️) - lower the better. Models are ranked based on their Average WER, from lowest to highest. Check the 📈 Metrics tab to understand how the models are evaluated. \ | |
| \nIf you want results for a model that is not listed here, you can submit a request for it to be included ✉️✨. \ | |
| \nThe leaderboard currently focuses on English speech recognition, and will be expanded to multilingual evaluation in later versions." | |
| CITATION_TEXT = """@misc{open-asr-leaderboard, | |
| title = {Open Automatic Speech Recognition Leaderboard}, | |
| author = {Srivastav, Vaibhav and Majumdar, Somshubra and Koluguri, Nithin and Moumen, Adel and Gandhi, Sanchit and others}, | |
| year = 2023, | |
| publisher = {Hugging Face}, | |
| howpublished = "\\url{https://huggingface.co/spaces/hf-audio/open_asr_leaderboard}" | |
| } | |
| """ | |
| METRICS_TAB_TEXT = """ | |
| Here you will find details about the speech recognition metrics and datasets reported in our leaderboard. | |
| ## Metrics | |
| 🎯 Word Error Rate (WER) and Real-Time Factor (RTF) are popular metrics for evaluating the accuracy of speech recognition | |
| models by estimating how accurate the predictions from the models are and how fast they are returned. We explain them each | |
| below. | |
| ### Word Error Rate (WER) | |
| Word Error Rate is used to measure the **accuracy** of automatic speech recognition systems. It calculates the percentage | |
| of words in the system's output that differ from the reference (correct) transcript. **A lower WER value indicates higher accuracy**. | |
| ``` | |
| Example: If the reference transcript is "I really love cats," and the ASR system outputs "I don't love dogs,". | |
| The WER would be `50%` because 2 out of 4 words are incorrect. | |
| ``` | |
| For a fair comparison, we calculate **zero-shot** (i.e. pre-trained models only) *normalised WER* for all the model checkpoints. You can find the evaluation code on our [Github repository](https://github.com/huggingface/open_asr_leaderboard). To read more about how the WER is computed, refer to the [Audio Transformers Course](https://huggingface.co/learn/audio-course/chapter5/evaluation). | |
| ### Real Time Factor (RTF) | |
| Real Time Factor is a measure of the **latency** of automatic speech recognition systems, i.e. how long it takes an | |
| model to process a given amount of speech. It's usually expressed as a multiple of real time. An RTF of 1 means it processes | |
| speech as fast as it's spoken, while an RTF of 2 means it takes twice as long. Thus, **a lower RTF value indicates lower latency**. | |
| ``` | |
| Example: If it takes an ASR system 10 seconds to transcribe 10 seconds of speech, the RTF is 1. | |
| If it takes 20 seconds to transcribe the same 10 seconds of speech, the RTF is 2. | |
| ``` | |
| For the benchmark, we report RTF averaged over a 10 minute audio sample that is chunked into 30 second segments, mimicking the [chunked long-form transcription strategy](https://huggingface.co/blog/asr-chunking) performed in Transfomrmers. We measure RTF on an A100 80GB GPU (Driver Version: 535.54.03, CUDA Version: 12.2), performing 5 warm-up runs and 3 graded runs, over which the RTF is averaged to get the final result. | |
| ## How to reproduce our results | |
| The ASR Leaderboard will be a continued effort to benchmark open source/access speech recognition models where possible. | |
| Along with the Leaderboard we're open-sourcing the codebase used for running these evaluations. | |
| For more details head over to our repo at: https://github.com/huggingface/open_asr_leaderboard | |
| P.S. We'd love to know which other models you'd like us to benchmark next. Contributions are more than welcome! ♥️ | |
| ## Benchmark datasets | |
| Evaluating Speech Recognition systems is a hard problem. We use the multi-dataset benchmarking strategy proposed in the | |
| [ESB paper](https://arxiv.org/abs/2210.13352) to obtain robust evaluation scores for each model. | |
| ESB is a benchmark for evaluating the performance of a single automatic speech recognition (ASR) system across a broad | |
| set of speech datasets. It comprises eight English speech recognition datasets, capturing a broad range of domains, | |
| acoustic conditions, speaker styles, and transcription requirements. As such, it gives a better indication of how | |
| a model is likely to perform on downstream ASR compared to evaluating it on one dataset alone. | |
| The ESB score is calculated as a macro-average of the WER scores across the ESB datasets. The models in the leaderboard | |
| are ranked based on their average WER scores, from lowest to highest. | |
| | Dataset | Domain | Speaking Style | Train (h) | Dev (h) | Test (h) | Transcriptions | License | | |
| |-----------------------------------------------------------------------------------------|-----------------------------|-----------------------|-----------|---------|----------|--------------------|-----------------| | |
| | [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | Audiobook | Narrated | 960 | 11 | 11 | Normalised | CC-BY-4.0 | | |
| | [Common Voice 9](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) | Wikipedia | Narrated | 1409 | 27 | 27 | Punctuated & Cased | CC0-1.0 | | |
| | [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | European Parliament | Oratory | 523 | 5 | 5 | Punctuated | CC0 | | |
| | [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | TED talks | Oratory | 454 | 2 | 3 | Normalised | CC-BY-NC-ND 3.0 | | |
| | [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | Audiobook, podcast, YouTube | Narrated, spontaneous | 2500 | 12 | 40 | Punctuated | apache-2.0 | | |
| | [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) | Financial meetings | Oratory, spontaneous | 4900 | 100 | 100 | Punctuated & Cased | User Agreement | | |
| | [Earnings-22](https://huggingface.co/datasets/revdotcom/earnings22) | Financial meetings | Oratory, spontaneous | 105 | 5 | 5 | Punctuated & Cased | CC-BY-SA-4.0 | | |
| | [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | Meetings | Spontaneous | 78 | 9 | 9 | Punctuated & Cased | CC-BY-4.0 | | |
| For more details on the individual datasets and how models are evaluated to give the ESB score, refer to the [ESB paper](https://arxiv.org/abs/2210.13352). | |
| """ | |