--- license: apache-2.0 language: ja tags: - audio - automatic-speech-recognition - hf-asr-leaderboard widget: - example_title: CommonVoice 8.0 (Test Split) src: >- https://huggingface.co/datasets/japanese-asr/ja_asr.common_voice_8_0/resolve/main/sample.flac - example_title: JSUT Basic 5000 src: >- https://huggingface.co/datasets/japanese-asr/ja_asr.jsut_basic5000/resolve/main/sample.flac - example_title: ReazonSpeech (Test Split) src: >- https://huggingface.co/datasets/japanese-asr/ja_asr.reazonspeech_test/resolve/main/sample.flac pipeline_tag: automatic-speech-recognition metrics: - wer model-index: - name: kotoba-tech/kotoba-whisper-v1.0 results: - task: type: automatic-speech-recognition dataset: name: CommonVoice_8.0 (Japanese) type: japanese-asr/ja_asr.common_voice_8_0 metrics: - name: WER type: WER value: 59.27 - name: CER type: CER value: 9.44 - task: type: automatic-speech-recognition dataset: name: ReazonSpeech (Test) type: japanese-asr/ja_asr.reazonspeech_test metrics: - name: WER type: WER value: 56.62 - name: CER type: CER value: 12.60 - task: type: automatic-speech-recognition dataset: name: JSUT Basic5000 type: japanese-asr/ja_asr.jsut_basic5000 metrics: - name: WER type: WER value: 64.36 - name: CER type: CER value: 8.48 --- # Kotoba-Whisper _Kotoba-Whisper_ is a collection of distilled [Whisper](https://arxiv.org/abs/2212.04356) models for Japanese ASR. Following the original work of distil-whisper ([Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430)), we employ OpenAI's [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3) as the teacher model, and the student model that consists the full encoder of the teacher whisper model, and a decoder with two layers initialized from the first and last layer of the whisper model. As the initial version, we release ***kotoba-whisper-v1.0*** trained on the `large` subset of [ReazonSpeech](https://huggingface.co/datasets/reazon-research/reazonspeech), which amounts 1,253 hours of audio with 16,861,235 characters of transcriptions (5 sec audio with 18 text tokens in average). Kotoba-whisper-v1.0 is competitive or even outpeform the largest whisper model in Japanese ASR benchmarks, while being 6.3 times faster than the whisper model. ## Table of Contents Since the sequential algorithm is the "de-facto" transcription algorithm across the most popular Whisper libraries (Whisper cpp, Faster-Whisper, OpenAI Whisper), this distilled model is designed to be compatible with these libraries. You can expect significant performance gains by switching from previous Distil-Whisper checkpoints to distil-large-v3 when using these libraries. For convenience, the weights for the most popular libraries are already converted, with instructions for getting started below. 1. [Evaluation Results](#evaluation-results) 2. [Transformers Usage](#transformers-usage) * [Short-Form Transcription](#short-form-transcription) * [Sequential Long-Form](#sequential-long-form) * [Chunked Long-Form](#chunked-long-form) * [Speculative Decoding](#speculative-decoding) * [Additional Speed and Memory Improvements](#additional-speed--memory-improvements) 2. [Library Integrations](#library-integrations) * [Whisper cpp](#whispercpp) * [Faster Whisper](#faster-whisper) 3. [Model Details](#model-details) ## Evaluation Results ***kotoba-whisper-v1.0*** achieves better CER and WER than the [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) in the in-domain held-out test set from ReazonSpeech, and achieves competitive CER and WER on the out-of-domain test set including [JSUT basic 5000](https://sites.google.com/site/shinnosuketakamichi/publication/jsut) and the Japanese subset from [CommonVoice 8.0](https://huggingface.co/datasets/common_voice). ### CER | Model | CommonVoice 8.0 (Japanese) | JSUT Basic 5000 | ReazonSpeech Test | |:------------------------------------------------------------------------------------------------|---------------------------:|----------------:|------------------:| | [***kotoba-tech/kotoba-whisper-v1.0***](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0) | 9.44 | 8.48 | 12.60 | | [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 8.52 | 7.18 | 15.18 | | [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 11.34 | 9.87 | 29.56 | | [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 15.26 | 14.22 | 34.29 | | [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 46.86 | 35.69 | 96.69 | ### WER | Model | CommonVoice 8.0 (Japanese) | JSUT Basic 5000 | ReazonSpeech Test | |:------------------------------------------------------------------------------------------------|---------------------------:|----------------:|------------------:| | [***kotoba-tech/kotoba-whisper-v1.0***](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0) | 59.27 | 64.36 | 56.62 | | [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 55.41 | 59.34 | 60.23 | | [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 63.64 | 69.52 | 76.04 | | [openai/whisper-small](https://huggingface.co/openai/whisper-small) | 74.21 | 82.02 | 82.99 | | [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) | 93.78 | 97.72 | 94.85 | ### Latency As kotoba-whisper uses the same architecture as [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3), it inherits the benefit of the improved latency compared to [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) (**6.3x faster than large-v3**, see the table below taken from [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3)). | Model | Params / M | Rel. Latency | |------------------------------------------------------------------------------|------------|--------------| | **[kotoba-tech/kotoba-whisper-v1.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0)**| **756** | **6.3** | | [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) | 1550 | 1.0 | ## Transformers Usage distil-large-v3 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first install the latest version of Transformers. For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub: ```bash pip install --upgrade pip pip install --upgrade transformers accelerate datasets[audio] ``` ### Short-Form Transcription The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe short-form audio files (< 30-seconds) as follows: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```diff - result = pipe(sample) + result = pipe("audio.mp3") ``` For segment-level timestamps, pass the argument `return_timestamps=True` and return the `"chunks"` output: ```python result = pipe(sample, return_timestamps=True) print(result["chunks"]) ```
For more control over the generation parameters, use the model + processor API directly: Ad-hoc generation arguments can be passed to `model.generate`, including `num_beams` for beam-search, `return_timestamps` for segment-level timestamps, and `prompt_ids` for prompting. See the [docstrings](https://huggingface.co/docs/transformers/en/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate) for more details. ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import Audio, load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate)) sample = dataset[0]["audio"] input_features = processor( sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt" ).input_features input_features = input_features.to(device, dtype=torch_dtype) gen_kwargs = { "max_new_tokens": 128, "num_beams": 1, "return_timestamps": False, } pred_ids = model.generate(input_features, **gen_kwargs) pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=gen_kwargs["return_timestamps"]) print(pred_text) ```
### Sequential Long-Form Unlike previous Distil-Whisper releases, distil-large-v3 is specifically designed to be compatible with OpenAI's sequential long-form transcription algorithm. This algorithm uses a sliding window for buffered inference of long audio files (> 30-seconds), and returns more accurate transcriptions compared to the [chunked long-form algorithm](#chunked-long-form). The sequential long-form algorithm should be used in either of the following scenarios: 1. Transcription accuracy is the most important factor, and latency is less of a consideration 2. You are transcribing **batches** of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate If you are transcribing single long audio files and latency is the most important factor, you should use the chunked algorithm described [below](#chunked-long-form). For a detailed explanation of the different algorithms, refer to Sections 5 of the [Distil-Whisper paper](https://arxiv.org/pdf/2311.00430.pdf). The [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class can be used to transcribe long audio files with the sequential algorithm as follows: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ```
For more control over the generation parameters, use the model + processor API directly: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import Audio, load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate)) sample = dataset[0]["audio"] inputs = processor( sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, ) inputs = inputs.to(device, dtype=torch_dtype) gen_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } pred_ids = model.generate(**i nputs, **gen_kwargs) pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False) print(pred_text) ```
### Chunked Long-Form distil-large-v3 remains compatible with the Transformers chunked long-form algorithm. This algorithm should be used when a single large audio file is being transcribed and the fastest possible inference is required. In such circumstances, the chunked algorithm is up to 9x faster than OpenAI's sequential long-form implementation (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/pdf/2311.00430.pdf)). To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For distil-large-v3, a chunk length of 25-seconds is optimal. To activate batching over long audio files, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=25, batch_size=16, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` ### Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Distil-Whisper to further reduce the inference speed and VRAM requirements. These optimisations primarily target the attention kernel, swapping it from an eager implementation to a more efficient flash attention version. #### Flash Attention 2 We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`: ```diff - model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="flash_attention_2") ``` #### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html). This attention implementation is activated **by default** for PyTorch versions 2.1.1 or greater. To check whether you have a compatible PyTorch version, run the following Python code snippet: ```python from transformers.utils import is_torch_sdpa_available print(is_torch_sdpa_available()) ``` If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/) Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying `attn_implementation="sdpa"` as follows: ```diff - model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True) + model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="sdpa") ``` ## Library Integrations ### Whisper.cpp Distil-Whisper can be run with the [Whisper.cpp](https://github.com/ggerganov/whisper.cpp) package with the original sequential long-form transcription algorithm. In a provisional benchmark on Mac M1, distil-large-v3 is over 5x faster than Whisper large-v3, while performing to within 0.8% WER over long-form audio. Steps for getting started: 1. Clone the Whisper.cpp repository: ``` git clone https://github.com/ggerganov/whisper.cpp.git cd whisper.cpp ``` 2. Install the Hugging Face Hub Python package: ```bash pip install --upgrade huggingface_hub ``` And download the GGML weights for distil-large-v3 using the following Python snippet: ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id='kotoba-tech/kotoba-whisper-v1.0-ggml', filename='ggml-distil-large-v3.bin', local_dir='./models') ``` Note that if you do not have a Python environment set-up, you can also download the weights directly with `wget`: ```bash wget https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml/resolve/main/ggml-distil-large-v3.bin -P ./models ``` 3. Run inference using the provided sample audio: ```bash make -j && ./main -m models/ggml-distil-large-v3.bin -f samples/jfk.wav ``` ### Faster-Whisper Faster-Whisper is a reimplementation of Whisper using [CTranslate2](https://github.com/OpenNMT/CTranslate2/), a fast inference engine for Transformer models. First, install the Faster-Whisper package according to the [official instructions](https://github.com/SYSTRAN/faster-whisper#installation). For this example, we'll also install 🤗 Datasets to load a toy audio dataset from the Hugging Face Hub: ```bash pip install --upgrade pip pip install --upgrade git+https://github.com/SYSTRAN/faster-whisper datasets[audio] ``` The following code snippet loads the distil-large-v3 model and runs inference on an example file from the LibriSpeech ASR dataset: ```python import torch from faster_whisper import WhisperModel from datasets import load_dataset # define our torch configuration device = "cuda:0" if torch.cuda.is_available() else "cpu" compute_type = "float16" if torch.cuda.is_available() else "float32" # load model on GPU if available, else cpu model = WhisperModel("distil-large-v3", device=device, compute_type=compute_type) # load toy dataset for example dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = dataset[1]["audio"]["path"] segments, info = model.transcribe(sample, beam_size=1) for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` To transcribe a local audio file, simply pass the path to the audio file as the `audio` argument to transcribe: ```python segments, info = model.transcribe("audio.mp3", beam_size=1) ``` ## Model Details Distil-Whisper inherits the encoder-decoder architecture from Whisper. The encoder maps a sequence of speech vector inputs to a sequence of hidden-state vectors. The decoder auto-regressively predicts text tokens, conditional on all previous tokens and the encoder hidden-states. Consequently, the encoder is only run forward once, whereas the decoder is run as many times as the number of tokens generated. In practice, this means the decoder accounts for over 90% of total inference time. Thus, to optimise for latency, the focus is on minimising the inference time of the decoder. To distill the Whisper model, we reduce the number of decoder layers while keeping the encoder fixed. The encoder (shown in green) is entirely copied from the teacher to the student and frozen during training. The student's decoder consists of a subset of the teacher decoder layers, which are intialised from maximally spaced layers. The model is then trained on a weighted sum of the KL divergence and pseudo-label loss terms.

## Evaluation The following code-snippets demonstrates how to evaluate the Distil-Whisper model on the LibriSpeech validation-clean dataset with [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet), meaning no audio data has to be downloaded to your local device. First, we need to install the required packages, including 🤗 Datasets to stream and load the audio data, and 🤗 Evaluate to perform the WER calculation: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] evaluate jiwer ``` Evaluation can then be run end-to-end with the following example: ```python from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import load_dataset from evaluate import load import torch from tqdm import tqdm # define our torch configuration device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "kotoba-tech/kotoba-whisper-v1.0" # load the model + processor model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, use_safetensors=True, low_cpu_mem_usage=True) model = model.to(device) processor = AutoProcessor.from_pretrained(model_id) # load the dataset with streaming mode dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True) # define the evaluation metric wer_metric = load("wer") def inference(batch): # 1. Pre-process the audio data to log-mel spectrogram inputs audio = [sample["array"] for sample in batch["audio"]] input_features = processor(audio, sampling_rate=batch["audio"][0]["sampling_rate"], return_tensors="pt").input_features input_features = input_features.to(device, dtype=torch_dtype) # 2. Auto-regressively generate the predicted token ids pred_ids = model.generate(input_features, max_new_tokens=128) # 3. Decode the token ids to the final transcription batch["transcription"] = processor.batch_decode(pred_ids, skip_special_tokens=True) batch["reference"] = batch["text"] return batch # batch size 16 inference dataset = dataset.map(function=inference, batched=True, batch_size=16) all_transcriptions = [] all_references = [] # iterate over the dataset and run inference for result in tqdm(dataset, desc="Evaluating..."): all_transcriptions.append(result["transcription"]) all_references.append(result["reference"]) # normalize predictions and references all_transcriptions = [processor.normalize(transcription) for transcription in all_transcriptions] all_references = [processor.normalize(reference) for reference in all_references] # compute the WER metric wer = 100 * wer_metric.compute(predictions=all_transcriptions, references=all_references) print(wer) ``` **Print Output:** ``` 2.428920763531516 ``` ## Data Distil-Whisper is trained on 22,000 hours of audio data from nine open-source, permissively licensed speech datasets on the Hugging Face Hub: | Dataset | Size / h | Speakers | Domain | Licence | |-----------------------------------------------------------------------------------------|----------|----------|-----------------------------|-----------------| | [People's Speech](https://huggingface.co/datasets/MLCommons/peoples_speech) | 12,000 | unknown | Internet Archive | CC-BY-SA-4.0 | | [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) | 3,000 | unknown | Narrated Wikipedia | CC0-1.0 | | [GigaSpeech](https://huggingface.co/datasets/speechcolab/gigaspeech) | 2,500 | unknown | Audiobook, podcast, YouTube | apache-2.0 | | Fisher | 1,960 | 11,900 | Telephone conversations | LDC | | [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) | 960 | 2,480 | Audiobooks | CC-BY-4.0 | | [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) | 540 | 1,310 | European Parliament | CC0 | | [TED-LIUM](https://huggingface.co/datasets/LIUM/tedlium) | 450 | 2,030 | TED talks | CC-BY-NC-ND 3.0 | | SwitchBoard | 260 | 540 | Telephone conversations | LDC | | [AMI](https://huggingface.co/datasets/edinburghcstr/ami) | 100 | unknown | Meetings | CC-BY-4.0 | |||||| | **Total** | 21,770 | 18,260+ | | | The combined dataset spans 10 distinct domains and over 50k speakers. The diversity of this dataset is crucial to ensuring the distilled model is robust to audio distributions and noise. The audio data is then pseudo-labelled using the Whisper large-v3 model: we use Whisper to generate predictions for all the audio in our training set and use these as the target labels during training. Using pseudo-labels ensures that the transcriptions are consistently formatted across datasets and provides sequence-level distillation signal during training. ## WER Filter The Whisper pseudo-label predictions are subject to mis-transcriptions and hallucinations. To ensure we only train on accurate pseudo-labels, we employ a simple WER heuristic during training. First, we normalise the Whisper pseudo-labels and the ground truth labels provided by each dataset. We then compute the WER between these labels. If the WER exceeds a specified threshold, we discard the training example. Otherwise, we keep it for training. Section 9.2 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) demonstrates the effectiveness of this filter for improving downstream performance of the distilled model. We also partially attribute Distil-Whisper's robustness to hallucinations to this filter. ## Training The model was trained for 80,000 optimisation steps (or 11 epochs) with batch size 256. The Tensorboard training logs can be found under: https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0/tensorboard?params=scalars#frame ## Results The distilled model performs to within 1.5% WER of Whisper large-v3 on out-of-distribution (OOD) short-form audio, within 1% WER on sequential long-form decoding, and outperforms large-v3 by 0.1% on chunked long-form. This performance gain is attributed to lower hallucinations. For a detailed per-dataset breakdown of the evaluation results, refer to Tables 16 and 17 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430) Distil-Whisper is also evaluated on the [ESB benchmark](https://arxiv.org/abs/2210.13352) datasets as part of the [OpenASR leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), where it performs to within 0.2% WER of Whisper. ## Reproducing Kotoba-Whisper Training and evaluation code to reproduce Kotoba-Whisper is available at the repository: [TBA](TBA). ## Acknowledgements * OpenAI for the Whisper [model](https://huggingface.co/openai/whisper-large-v3). * Hugging Face 🤗 [Transformers](https://github.com/huggingface/transformers) for the model integration. * Hugging Face 🤗 for sharing the [Distil-Whisper codebase](https://github.com/huggingface/distil-whisper).