From b69fd04345d9bb879e28a9633ce9d1ff5c810126 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Mon, 29 Jul 2024 12:09:47 +0900 Subject: [PATCH 01/13] Deprecate 'djlint' from requirements.txt As of Jul 2024, I could not install djlint on Ubuntu 22.04. It causes an error during installation of its dependencies (in particular, cssbeautifier). Let's remove it from the dependency list. Signed-off-by: Fujimoto Seiji --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c13f949..d535b5b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,5 +3,4 @@ Pygments==2.13.0 myst-parser==0.18.1 furo==2022.12.7 sphinxcontrib-googleanalytics -djlint -e ./pkg/reazon_theme \ No newline at end of file From ac5d5f1ab79a06f1683803f56a16cdcc420308e5 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Sun, 28 Jul 2024 22:38:06 +0900 Subject: [PATCH 02/13] Add API documentation for 'reazonspeech.k2.asr' Signed-off-by: Fujimoto Seiji --- source/projects/ReazonSpeech/api/index.rst | 1 + .../ReazonSpeech/api/reazonspeech.k2.asr.rst | 140 ++++++++++++++++++ 2 files changed, 141 insertions(+) create mode 100644 source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst diff --git a/source/projects/ReazonSpeech/api/index.rst b/source/projects/ReazonSpeech/api/index.rst index d71a0c9..e3f81a4 100644 --- a/source/projects/ReazonSpeech/api/index.rst +++ b/source/projects/ReazonSpeech/api/index.rst @@ -11,3 +11,4 @@ ReazonSpeechでは、音声処理を行うための様々なPythonインター reazonspeech.nemo.asr.rst reazonspeech.espnet.asr.rst reazonspeech.espnet.oneseg.rst + reazonspeech.k2.asr.rst diff --git a/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst b/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst new file mode 100644 index 0000000..e990558 --- /dev/null +++ b/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst @@ -0,0 +1,140 @@ +=================== +reazonspeech.k2.asr +=================== + +.. py:module:: reazonspeech.k2.asr + +このリファレンスでは、K2モデルで音声認識するためのインターフェイスを解説します。 + +関数 +==== + +.. function:: load_model(device="cpu", precision="fp32") + + ReazonSpeechのK2モデルをロードする。 + + :param device: ``cuda``, ``cpu`` または ``coreml`` + :param precision: ``fp32`` または ``int8`` + :rtype: sherpa_onnx.OfflineRecognizer + +.. function:: transcribe(model, audio, config=None) + + ReazonSpeechモデルで音声を認識し、結果を返却する。 + + **サンプルコード** + + .. code:: python3 + + from reazonspeech.k2.asr import audio_from_path, load_model, transcribe + + audio = audio_from_path("test.wav") + model = load_model() + ret = transcribe(model, audio) + + print('TEXT:') + print(' -', ret.text) + + print('SUBWORDS:') + for subword in ret.subwords[:9]: + print(' -', subword) + + **実行結果** + + .. code:: yaml + + TEXT: + - ヤンバルクイナとの出会いは十八歳の時だった + SUBWORDS: + - Subword(seconds=0.03, token='ヤ') + - Subword(seconds=1.36, token='ン') + - Subword(seconds=1.55, token='バ') + - Subword(seconds=1.75, token='ル') + - Subword(seconds=1.91, token='ク') + - Subword(seconds=2.11, token='イ') + - Subword(seconds=2.27, token='ナ') + - Subword(seconds=2.51, token='と') + - Subword(seconds=2.67, token='の') + + :param sherpa_onnx.OfflineRecognizer model: ReazonSpeechモデル + :param AudioData audio: 音声データ + :param TranscribeConfig config: 追加オプション(省略可) + :rtype: TranscribeResult + +補助関数 +======== + +.. function:: audio_from_path(path) + + 音声ファイルを読み込み、音声データを返却する。 + + :param str path: 音声ファイルのパス + :rtype: AudioData + +.. function:: audio_from_numpy(array, samplerate) + + Numpyの配列を受け取り、音声データを返却する。 + + :param array numpy.ndarray: 音声データ + :param samplerate int: サンプリングレート + :rtype: AudioData + +.. function:: audio_from_tensor(tensor, samplerate) + + PyTorchのテンソルを受け取り、音声データを返却する。 + + :param array torch.tensor: 音声データ + :param samplerate int: サンプリングレート + :rtype: AudioData + +クラス +====== + +.. class:: TranscribeConfig + + 音声認識の処理を調整するための設定値クラス + + .. attribute:: verbose + :type: bool + :value: True + +.. class:: TranscribeResult + + 音声認識の結果を格納するためのデータクラス + + .. attribute:: text + :type: str + + 音声認識結果の文字列 + + .. attribute:: subwords + :type: List[Subword] + + サブワード単位のタイムスタンプ情報 + +.. class:: Subword + + サブワード単位の認識結果 + + .. attribute:: seconds + :type: float + + サブワードの出現時刻 + + .. attribute:: token + :type: str + + サブワード文字列 + +.. class:: AudioData + + 音声データを格納するためのコンテナ + + .. attribute:: waveform + :type: numpy.array + + 音声データ + + .. attribute:: samplerate + :type: int + + サンプリングレート From 0396dbe730a4bb46522921c9574e600f8baeb5f6 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Sun, 28 Jul 2024 22:41:27 +0900 Subject: [PATCH 03/13] Add a link to ReazonSpeech K2 model Signed-off-by: Fujimoto Seiji --- source/projects/ReazonSpeech/index.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/source/projects/ReazonSpeech/index.rst b/source/projects/ReazonSpeech/index.rst index 713711d..46c9672 100644 --- a/source/projects/ReazonSpeech/index.rst +++ b/source/projects/ReazonSpeech/index.rst @@ -22,7 +22,9 @@ ReazonSpeech - `Apache-2.0`_ - `reazonspeech-nemo-v2 `_ - `reazonspeech-espnet-v2 `_ + `reazonspeech-k2-v2 `_ + + `reazonspeech-espnet-v2 `_ * - 音声処理ライブラリ - `Apache-2.0`_ @@ -40,8 +42,6 @@ ReazonSpeech .. _Apache-2.0: https://www.apache.org/licenses/LICENSE-2.0 .. _CDLA-Sharing-1.0: https://cdla.dev/sharing-1-0/ -.. _reazonspeech-demo: - ReazonSpeechコーパスのサンプル音声 ================================== From 7e94f072395576d0848a8f04682f4fe8ae2461e1 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Sun, 28 Jul 2024 22:38:40 +0900 Subject: [PATCH 04/13] Add how-to guides for each ReazonSpeech model This also introduces the top-level categories such as "ASR" to sort out sections. I think it helps, since the number of how-to guides has grown a bit. Signed-off-by: Fujimoto Seiji --- source/projects/ReazonSpeech/howto.rst | 129 +++++++++++++++++++++---- 1 file changed, 112 insertions(+), 17 deletions(-) diff --git a/source/projects/ReazonSpeech/howto.rst b/source/projects/ReazonSpeech/howto.rst index 760e91d..3e7d559 100644 --- a/source/projects/ReazonSpeech/howto.rst +++ b/source/projects/ReazonSpeech/howto.rst @@ -4,8 +4,11 @@ HowToガイド .. _reazonspeech-corpus: +音声コーパス +============ + 日本語音声コーパスにアクセスする -================================ +-------------------------------- .. important:: @@ -118,10 +121,13 @@ ReazonSpeechでは5種類のデータセットのサイズを提供していま .. _nemo-asr: -Pythonから音声認識モデルを利用する -================================== +音声認識 +======== + +NeMoモデルで日本語の音声を認識する +---------------------------------- -ReazonSpeechの音声モデルを利用して、Pythonから音声認識を行う方法を解説します。 +ReazonSpeechのNeMoモデルを利用して、Pythonから音声認識を行う方法を解説します。 .. list-table:: :widths: 2 3 @@ -171,24 +177,115 @@ ReazonSpeechの音声モデルを利用して、Pythonから音声認識を行 $ python3 test.py 気象庁は雪や路面の凍結による交通への影響、暴風雪や高波に警戒するとともに雪崩や屋根からの落雪にも十分注意するよう呼びかけています。 -関数が受け取る引数や返り値の詳細はAPIリファレンス :any:`api/reazonspeech.nemo.asr` を参照ください。 +各関数の詳細な使い方は、APIリファレンス :any:`api/reazonspeech.nemo.asr` を参照ください。 + +K2モデルで日本語の音声を認識する +-------------------------------- + +ReazonSpeechのK2モデルを利用して、Pythonから音声認識を行う方法を解説します。 + +* K2モデルを利用するには `shepra-onnx `_ (K2モデルの評価エンジン)が必須です。 +* また、K2モデルが扱える入力音声の長さは、概ね30秒が上限になります。 + +.. list-table:: + :widths: 2 3 + + * - 最初に、sherpa-onnxの公式サイトの手順に従って、パッケージをインストールします。 + + - `shepra-onnx - Install Python package `_ + + * - ReazonSpeechをインストールします。 + + - .. code:: console + + $ git clone https://github.com/reazon-research/ReazonSpeech + $ pip install ReazonSpeech/pkg/k2-asr + + * - 右のスクリプトを ``test.py`` という名前で保存します。 + + * サンプル音源: :download:`speech-001.wav <../../_static/speech-001.wav>` + + - .. code:: python + + from reazonspeech.k2.asr import load_model, transcribe, audio_from_path + + # 実行時にHugging Faceからモデルを取得します (1.5GB) + model = load_model(device='cuda') + + # ローカルの音声ファイルを読み込む + audio = audio_from_path('speech-001.wav') + + # 音声認識を適用する + ret = transcribe(model, audio) + + print(ret.text) + + * - 結果が出力されれば成功です! + + - .. code:: console + + $ python3 test.py + 気象庁は雪や路面の凍結による交通への影響暴風雪や高波に警戒するとともに雪崩や屋根からの落雪にも十分注意するよう呼びかけています + +各関数の詳細な使い方は、APIリファレンス :any:`api/reazonspeech.k2.asr` を参照ください。 + +ESPnetモデルで日本語の音声を認識する +------------------------------------ -.. note:: +ReazonSpeechのESPnetモデルを利用して、Pythonから音声認識を行う方法を解説します。 - ReazonSpeechはESPnetとNeMoの2種類のモデルを提供しています。 +.. list-table:: + :widths: 2 3 - ESPnetバージョンの音声認識モデルを利用する場合は、 - 最初のインストールのステップで次のコマンドを実行します。 - .. code:: console + * - 実行環境をセットアップします。 - $ git clone https://github.com/reazon-research/ReazonSpeech - $ pip install ReazonSpeech/pkg/espnet-asr + - .. code:: console - 詳細はAPIリファレンス :any:`reazonspeech.espnet.asr` を参照ください。 + $ # Pythonのvenv環境作成 + $ python3 -m venv venv + $ source venv/bin/activate + + * - ReazonSpeechをインストールします。 + + - .. code:: console + + $ git clone https://github.com/reazon-research/ReazonSpeech + $ pip install ReazonSpeech/pkg/espnet-asr + + * - 右のスクリプトを ``test.py`` という名前で保存します。 + + * サンプル音源: :download:`speech-001.wav <../../_static/speech-001.wav>` + + - .. code:: python + + from reazonspeech.espnet.asr import load_model, transcribe, audio_from_path + + # 実行時にHugging Faceからモデルを取得します (1.6GB) + model = load_model(device='cuda') + + # ローカルの音声ファイルを読み込む + audio = audio_from_path('speech-001.wav') + + # 音声認識を適用する + ret = transcribe(model, audio) + + print(ret.text) + + * - 結果が出力されれば成功です! + + - .. code:: console + + $ python3 test.py + 気象庁は雪や路面の凍結による交通への影響、暴風雪や高波に警戒するとともに雪崩や屋根からの落雪にも十分注意するよう呼びかけています。 + +各関数の詳細な使い方は、APIリファレンス :any:`api/reazonspeech.espnet.asr` を参照ください。 + +データ解析 +========== ワンセグ放送から字幕情報を抽出する -================================== +---------------------------------- .. list-table:: :widths: 2 3 @@ -217,10 +314,8 @@ ReazonSpeechの音声モデルを利用して、Pythonから音声認識を行 end_seconds=5.1291, text='今日のニュースをお伝えします') - - ワンセグ放送からコーパスを作成する 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announce ReazonSpeech v2.1, which sets new +records in Japanese ASR benchmark tests. The new model is built on +`Next-gen Kaldi framework `_, and distributed in +platform-neutral `Open Neural Network Exchange (ONNX) format `_, +and support quantization for very low memory footprint. + +We published the ReazonSpeech v2.1 model under the Apache 2.0 license. The +model files and the inference code are readily available on Hugging Face +and GitHub. + +**Hugging Face** + +https://huggingface.co/reazon-research/reazonspeech-k2-v2 + +**GitHub** + +https://github.com/reazon-research/ReazonSpeech + +What is ReazonSpeech v2.1? +========================== + +ReazonSpeech v2.1 represents the latest iteration of Reazon Human Interaction +Lab's ASR research. This release introduces a new Japanese ASR model that: + +* Outperforms existing Japanese ASR models on JSUT-BASIC5000 [#jsut-basic5000]_, + Common Voice v8.0 [#cv]_, and TEDxJP-10K [#tedx]_ benchmark sets (see the + chart below). + +* Excels in compactness, only having 159M parameters. + +* Excels in inference speed, one of the fastest models to process short audio inputs. + +What enables such outstanding performance is the state-of-the-art Transformer +called Zipformer [#zipformer]_. We trained this novel network architecture on +35,000 hours of `Reazonspeech v2.0 corpus +`_, +which revealed a best-in-class performance. + +.. tip:: + + For further details about the ReazonSpeech v2.1 model, the full training + recipe is available on `k2-fsa/icefall `_. + +.. rubric:: Figure: ReazonSpeech v2.1 on common Japanese ASR benchmark tests + +.. figure:: ../_static/blog/2024-08-01-ReazonSpeech/cer.png + +Easy deployment with ONNX +========================= + +As already mentioned, the ReazonSpeech v2.1 model is available in ONNX format. + +Since ONNX runtime covers the very wide range of platforms and does not +depend on PyTorch runtime, this significantly reduces the setup process, +making it practical to use in various devices, including Linux, macOS, Windows, +embedded systems, Android, and iOS. + +For more details about the supported platforms, please refer to the +`Sherpa-ONNX's documentation `_. + +Reduce memory footprint with quantization +========================================= + +We also released ``int8``-quantized version of the ReazonSpeech v2.1 model. +The quantized model exhibits a significantly smaller footprint, as shown +in the following table. + +============ ================ ================ +FILE FILE SIZE (FP32) FILE SIZE (INT8) +============ ================ ================ +Encoder 565 MB 148 MB +Decoder 12 MB 3 MB +Joiner 11 MB 3 MB +============ ================ ================ + +This should enable to deploy our model even on devices with very limited +computational capacity. + +Conclusion +========== + +We believe this release marks a significantly milestone in Japanese ASR, +enabling anyone to deploy our best-in-class ASR model to a very wide +range of platforms. + +We are looking forward to receiving feedbacks from users who makes use +of our latest research results. + +Footnotes +========= + +.. [#jsut-basic5000] Ryosuke Sonobe, Shinnosuke Takamichi and Hiroshi Saruwatari, "JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis," arXiv preprint, 1711.00354, 2017. +.. [#cv] https://commonvoice.mozilla.org/ +.. [#tedx] https://github.com/laboroai/TEDxJP-10K +.. 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transparent;outline-offset:2px}.dark .Nav .Nav__button{background-color:rgb(0 0 0 / var(--tw-bg-opacity));--tw-bg-opacity: .3;--tw-text-opacity: 1;color:rgb(255 255 255 / var(--tw-text-opacity))}.Nav .Nav__button{-webkit-backdrop-filter:blur(2px);backdrop-filter:blur(2px)}.Nav__logo img{width:163px} From 113558ce8b13d34eafe8aa508bef6b7552c7a6db Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 09:57:52 +0900 Subject: [PATCH 08/13] Update the latest updates on the top page Signed-off-by: Fujimoto Seiji --- source/index.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/source/index.rst b/source/index.rst index 43460e2..0357bf4 100644 --- a/source/index.rst +++ b/source/index.rst @@ -6,6 +6,8 @@ Reazon Human Interaction Laboratory .. list-table:: + * - 2024年8月1日 + - :any:`ReazonSpeechの最新バージョン v2.1 をリリースしました。 ` * - 2024年2月14日 - :any:`ReazonSpeechの最新バージョン v2.0 をリリースしました。 ` * - 2023年6月15日 @@ -18,6 +20,8 @@ Reazon Human Interaction Laboratory 最新記事 -------- +* :any:`blog/2024-08-01-ReazonSpeech` +* :any:`blog/2024-03-02-how-to-run-aloha-developers.agirobots.com` * :any:`blog/2024-02-14-ReazonSpeech` * :any:`blog/2023-04-04-ReazonSpeech` * :any:`blog/2023-01-15-DDS-performance` From 747902320a907f9d8ec36467dd1e6d9b334e7f69 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 13:27:53 +0900 Subject: [PATCH 09/13] Fix up '2024-08-01-ReazonSpeech.rst' Suggestion from Bailey Hirota. This fixes the various parts that sound awkword. Signed-off-by: Fujimoto Seiji --- source/blog/2024-08-01-ReazonSpeech.rst | 29 +++++++++++++++---------- 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/source/blog/2024-08-01-ReazonSpeech.rst b/source/blog/2024-08-01-ReazonSpeech.rst index 1448bc7..6436d55 100644 --- a/source/blog/2024-08-01-ReazonSpeech.rst +++ b/source/blog/2024-08-01-ReazonSpeech.rst @@ -6,7 +6,7 @@ Today, we're excited to announce ReazonSpeech v2.1, which sets new records in Japanese ASR benchmark tests. The new model is built on `Next-gen Kaldi framework `_, and distributed in platform-neutral `Open Neural Network Exchange (ONNX) format `_, -and support quantization for very low memory footprint. +and it also supports quantization for very low memory footprint. We published the ReazonSpeech v2.1 model under the Apache 2.0 license. The model files and the inference code are readily available on Hugging Face @@ -45,19 +45,20 @@ which revealed a best-in-class performance. For further details about the ReazonSpeech v2.1 model, the full training recipe is available on `k2-fsa/icefall `_. -.. rubric:: Figure: ReazonSpeech v2.1 on common Japanese ASR benchmark tests - .. figure:: ../_static/blog/2024-08-01-ReazonSpeech/cer.png + **Figure: ReazonSpeech v2.1 on common Japanese ASR benchmark tests** + Easy deployment with ONNX ========================= -As already mentioned, the ReazonSpeech v2.1 model is available in ONNX format. - -Since ONNX runtime covers the very wide range of platforms and does not -depend on PyTorch runtime, this significantly reduces the setup process, -making it practical to use in various devices, including Linux, macOS, Windows, -embedded systems, Android, and iOS. +As previously stated, the ReazonSpeech v2.1 model is now available in the ONNX +format, significantly enhancing its versatility across a wide range of +platforms. Leveraging the ONNX runtime, which is independent of the PyTorch +framework, simplifies the setup process, facilitating seamless integration +across diverse environments. This adaptability ensures practical application on +various devices, including Linux, macOS, Windows, embedded systems, Android, +and iOS. For more details about the supported platforms, please refer to the `Sherpa-ONNX's documentation `_. @@ -65,7 +66,7 @@ For more details about the supported platforms, please refer to the Reduce memory footprint with quantization ========================================= -We also released ``int8``-quantized version of the ReazonSpeech v2.1 model. +We also released a ``int8``-quantized version of the ReazonSpeech v2.1 model. The quantized model exhibits a significantly smaller footprint, as shown in the following table. @@ -77,8 +78,12 @@ Decoder 12 MB 3 MB Joiner 11 MB 3 MB ============ ================ ================ -This should enable to deploy our model even on devices with very limited -computational capacity. +These quantized models are up to 4x smaller than comparable ASR models like +Whisper-Large-v3, enabling their deployment on a wide range of devices with +computational constraints. Notably, when used with a non-quantized decoder, +these quantized models maintain accuracy levels comparable to their +non-quantized counterparts. This should enable the deployment of our model even +on devices with very limited computational capacity. Conclusion ========== From 462a548ee8fea7281bbd9cedd61c9105aa067bf7 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 13:48:17 +0900 Subject: [PATCH 10/13] Anoter fix on '2024-08-01-ReazonSpeech.rst' Patch from Kinan Martin. Notably, this adds a comparison table for quantized models, which shows that the `int8-fp32` mode archives a good tradeoff between accuracy and memory footprint. Signed-off-by: Fujimoto Seiji --- source/blog/2024-08-01-ReazonSpeech.rst | 69 ++++++++++++++++--------- 1 file changed, 46 insertions(+), 23 deletions(-) diff --git a/source/blog/2024-08-01-ReazonSpeech.rst b/source/blog/2024-08-01-ReazonSpeech.rst index 6436d55..8dd3d5e 100644 --- a/source/blog/2024-08-01-ReazonSpeech.rst +++ b/source/blog/2024-08-01-ReazonSpeech.rst @@ -2,11 +2,14 @@ (2024-08-01) ReazonSpeech v2.1: Setting a New Standard in Japanese ASR ====================================================================== -Today, we're excited to announce ReazonSpeech v2.1, which sets new -records in Japanese ASR benchmark tests. The new model is built on +Today, we're excited to announce ReazonSpeech v2.1. In this release, we +publish ReazonSpeech-k2-v2, an open-source Japanese ASR model which sets +new records in benchmark tests. It is built on the `Next-gen Kaldi framework `_, and distributed in -platform-neutral `Open Neural Network Exchange (ONNX) format `_, -and it also supports quantization for very low memory footprint. +the platform-neutral +`Open Neural Network Exchange (ONNX) format `_. +ReazonSpeech-k2-v2 excels in accuracy, compactness, and inference speed, +and can run on-device without GPU. We published the ReazonSpeech v2.1 model under the Apache 2.0 license. The model files and the inference code are readily available on Hugging Face @@ -52,13 +55,12 @@ which revealed a best-in-class performance. Easy deployment with ONNX ========================= -As previously stated, the ReazonSpeech v2.1 model is now available in the ONNX -format, significantly enhancing its versatility across a wide range of -platforms. Leveraging the ONNX runtime, which is independent of the PyTorch -framework, simplifies the setup process, facilitating seamless integration -across diverse environments. This adaptability ensures practical application on -various devices, including Linux, macOS, Windows, embedded systems, Android, -and iOS. +The ReazonSpeech-k2-v2 model is available in the ONNX format, significantly +enhancing its versatility across a wide range of platforms. Leveraging the ONNX +runtime, which is independent of the PyTorch framework, simplifies the setup +process, facilitating seamless integration across diverse environments. This +adaptability ensures practical application on various devices, including Linux, +macOS, Windows, embedded systems, Android, and iOS. For more details about the supported platforms, please refer to the `Sherpa-ONNX's documentation `_. @@ -82,18 +84,39 @@ These quantized models are up to 4x smaller than comparable ASR models like Whisper-Large-v3, enabling their deployment on a wide range of devices with computational constraints. Notably, when used with a non-quantized decoder, these quantized models maintain accuracy levels comparable to their -non-quantized counterparts. This should enable the deployment of our model even -on devices with very limited computational capacity. - -Conclusion -========== - -We believe this release marks a significantly milestone in Japanese ASR, -enabling anyone to deploy our best-in-class ASR model to a very wide -range of platforms. - -We are looking forward to receiving feedbacks from users who makes use -of our latest research results. +non-quantized counterparts. This enables the deployment of our model even on +devices with very limited computational capacity. + +============================== ======= ============ ========== +Model Name JSUT Common Voice TEDxJP-10K +============================== ======= ============ ========== +ReazonSpeech-k2-v2 6.45 7.85 9.09 +ReazonSpeech-k2-v2 (int8) 6.63 8.20 9.85 +ReazonSpeech-k2-v2 (fp32-int8) 6.46 7.88 9.15 +Whisper Large-v3 7.18 8.18 9.96 +ReazonSpeech-NeMo-v2 7.31 8.81 10.42 +ReazonSpeech-ESPnet-v2 6.89 8.27 9.28 +============================== ======= ============ ========== + +Future goals +============ + +With this release, we have significantly enhanced both the speed and accuracy +of our Japanese ASR models. By making our model open-source on the K2 +Sherpa-ONNX platform, we have greatly improved accessibility for a broad range +of users and developers across various platforms. + +Looking ahead, we are committed to further advancing our models by expanding +our dataset, developing streaming ASR capabilities, and incorporating +multilingual data to create an exceptional bilingual English-Japanese ASR +model. + +This release represents a major milestone, and we are excited to continue +pushing the boundaries of Japanese speech processing technology in the future. +Currently, ReazonSpeech-k2-v2 can process longer segments of audio with the +help of voice activity detection (VAD). In the future, we plan to release a +streaming version of this model which can innately support real-time +transcription. Footnotes ========= From ccf4c03937c287997296e35d134f1f8a356f9da5 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 14:57:34 +0900 Subject: [PATCH 11/13] Add captions to tables in 'Reduce memory footprint with quantization' Signed-off-by: Fujimoto Seiji --- source/blog/2024-08-01-ReazonSpeech.rst | 42 ++++++++++++++----------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/source/blog/2024-08-01-ReazonSpeech.rst b/source/blog/2024-08-01-ReazonSpeech.rst index 8dd3d5e..7b33e48 100644 --- a/source/blog/2024-08-01-ReazonSpeech.rst +++ b/source/blog/2024-08-01-ReazonSpeech.rst @@ -5,7 +5,7 @@ Today, we're excited to announce ReazonSpeech v2.1. In this release, we publish ReazonSpeech-k2-v2, an open-source Japanese ASR model which sets new records in benchmark tests. It is built on the -`Next-gen Kaldi framework `_, and distributed in +`Next-gen Kaldi framework `_ and distributed in the platform-neutral `Open Neural Network Exchange (ONNX) format `_. ReazonSpeech-k2-v2 excels in accuracy, compactness, and inference speed, @@ -50,7 +50,7 @@ which revealed a best-in-class performance. .. figure:: ../_static/blog/2024-08-01-ReazonSpeech/cer.png - **Figure: ReazonSpeech v2.1 on common Japanese ASR benchmark tests** + **Figure 1: ReazonSpeech v2.1 on common Japanese ASR benchmark tests** Easy deployment with ONNX ========================= @@ -72,13 +72,15 @@ We also released a ``int8``-quantized version of the ReazonSpeech v2.1 model. The quantized model exhibits a significantly smaller footprint, as shown in the following table. -============ ================ ================ -FILE FILE SIZE (FP32) FILE SIZE (INT8) -============ ================ ================ -Encoder 565 MB 148 MB -Decoder 12 MB 3 MB -Joiner 11 MB 3 MB -============ ================ ================ +.. table:: Table 1: The effects of quantization on model size + + ============ ================ ================ + FILE FILE SIZE (FP32) FILE SIZE (INT8) + ============ ================ ================ + Encoder 565 MB 148 MB + Decoder 12 MB 3 MB + Joiner 11 MB 3 MB + ============ ================ ================ These quantized models are up to 4x smaller than comparable ASR models like Whisper-Large-v3, enabling their deployment on a wide range of devices with @@ -87,16 +89,18 @@ these quantized models maintain accuracy levels comparable to their non-quantized counterparts. This enables the deployment of our model even on devices with very limited computational capacity. -============================== ======= ============ ========== -Model Name JSUT Common Voice TEDxJP-10K -============================== ======= ============ ========== -ReazonSpeech-k2-v2 6.45 7.85 9.09 -ReazonSpeech-k2-v2 (int8) 6.63 8.20 9.85 -ReazonSpeech-k2-v2 (fp32-int8) 6.46 7.88 9.15 -Whisper Large-v3 7.18 8.18 9.96 -ReazonSpeech-NeMo-v2 7.31 8.81 10.42 -ReazonSpeech-ESPnet-v2 6.89 8.27 9.28 -============================== ======= ============ ========== +.. table:: Table 2: The effects of quantization on accuracy + + ============================== ======= ============ ========== + Model Name JSUT Common Voice TEDxJP-10K + ============================== ======= ============ ========== + ReazonSpeech-k2-v2 6.45 7.85 9.09 + ReazonSpeech-k2-v2 (int8) 6.63 8.20 9.85 + ReazonSpeech-k2-v2 (int8-fp32) 6.46 7.88 9.15 + Whisper Large-v3 7.18 8.18 9.96 + ReazonSpeech-NeMo-v2 7.31 8.81 10.42 + ReazonSpeech-ESPnet-v2 6.89 8.27 9.28 + ============================== ======= ============ ========== Future goals ============ From 2375e6e8abd53b46a0a585895de2b0e1885bd692 Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 15:11:43 +0900 Subject: [PATCH 12/13] More updates on '2024-08-01-ReazonSpeech.rst' Signed-off-by: Fujimoto Seiji --- source/blog/2024-08-01-ReazonSpeech.rst | 36 +++++++++++-------------- 1 file changed, 15 insertions(+), 21 deletions(-) diff --git a/source/blog/2024-08-01-ReazonSpeech.rst b/source/blog/2024-08-01-ReazonSpeech.rst index 7b33e48..eae053f 100644 --- a/source/blog/2024-08-01-ReazonSpeech.rst +++ b/source/blog/2024-08-01-ReazonSpeech.rst @@ -11,17 +11,15 @@ the platform-neutral ReazonSpeech-k2-v2 excels in accuracy, compactness, and inference speed, and can run on-device without GPU. -We published the ReazonSpeech v2.1 model under the Apache 2.0 license. The -model files and the inference code are readily available on Hugging Face -and GitHub. +We published the ReazonSpeech-k2-v2 model under the Apache 2.0 license. The +model files and the inference code are readily available on +`Hugging Face `_ +and +`GitHub `_. -**Hugging Face** - -https://huggingface.co/reazon-research/reazonspeech-k2-v2 - -**GitHub** +.. figure:: ../_static/blog/2024-08-01-ReazonSpeech/cer.png -https://github.com/reazon-research/ReazonSpeech + **Figure 1: ReazonSpeech v2.1 on common Japanese ASR benchmark tests** What is ReazonSpeech v2.1? ========================== @@ -31,7 +29,7 @@ Lab's ASR research. This release introduces a new Japanese ASR model that: * Outperforms existing Japanese ASR models on JSUT-BASIC5000 [#jsut-basic5000]_, Common Voice v8.0 [#cv]_, and TEDxJP-10K [#tedx]_ benchmark sets (see the - chart below). + chart above). * Excels in compactness, only having 159M parameters. @@ -45,13 +43,9 @@ which revealed a best-in-class performance. .. tip:: - For further details about the ReazonSpeech v2.1 model, the full training + For further details about the ReazonSpeech-k2-v2 model, the full training recipe is available on `k2-fsa/icefall `_. -.. figure:: ../_static/blog/2024-08-01-ReazonSpeech/cer.png - - **Figure 1: ReazonSpeech v2.1 on common Japanese ASR benchmark tests** - Easy deployment with ONNX ========================= @@ -59,8 +53,8 @@ The ReazonSpeech-k2-v2 model is available in the ONNX format, significantly enhancing its versatility across a wide range of platforms. Leveraging the ONNX runtime, which is independent of the PyTorch framework, simplifies the setup process, facilitating seamless integration across diverse environments. This -adaptability ensures practical application on various devices, including Linux, -macOS, Windows, embedded systems, Android, and iOS. +adaptability ensures practical application on various devices even without GPU, +including Linux, macOS, Windows, embedded systems, Android, and iOS. For more details about the supported platforms, please refer to the `Sherpa-ONNX's documentation `_. @@ -68,7 +62,7 @@ For more details about the supported platforms, please refer to the Reduce memory footprint with quantization ========================================= -We also released a ``int8``-quantized version of the ReazonSpeech v2.1 model. +We also released a ``int8``-quantized version of the ReazonSpeech-k2-v2 model. The quantized model exhibits a significantly smaller footprint, as shown in the following table. @@ -82,7 +76,7 @@ in the following table. Joiner 11 MB 3 MB ============ ================ ================ -These quantized models are up to 4x smaller than comparable ASR models like +These quantized models are up to 10x smaller than comparable ASR models like Whisper-Large-v3, enabling their deployment on a wide range of devices with computational constraints. Notably, when used with a non-quantized decoder, these quantized models maintain accuracy levels comparable to their @@ -95,8 +89,8 @@ devices with very limited computational capacity. Model Name JSUT Common Voice TEDxJP-10K ============================== ======= ============ ========== ReazonSpeech-k2-v2 6.45 7.85 9.09 - ReazonSpeech-k2-v2 (int8) 6.63 8.20 9.85 - ReazonSpeech-k2-v2 (int8-fp32) 6.46 7.88 9.15 + ReazonSpeech-k2-v2 (int8) 6.63 8.19 9.86 + ReazonSpeech-k2-v2 (int8-fp32) 6.45 7.87 9.15 Whisper Large-v3 7.18 8.18 9.96 ReazonSpeech-NeMo-v2 7.31 8.81 10.42 ReazonSpeech-ESPnet-v2 6.89 8.27 9.28 From 22cae498e13ed08ad662e2e11eab6e6acd25724f Mon Sep 17 00:00:00 2001 From: Fujimoto Seiji Date: Thu, 1 Aug 2024 16:21:36 +0900 Subject: [PATCH 13/13] Document the mixed-precision mode of reazonspeech.k2.asr This also fix the order of API documentation links. Signed-off-by: Fujimoto Seiji --- source/projects/ReazonSpeech/api/index.rst | 2 +- source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/source/projects/ReazonSpeech/api/index.rst b/source/projects/ReazonSpeech/api/index.rst index e3f81a4..3d7c9f6 100644 --- a/source/projects/ReazonSpeech/api/index.rst +++ b/source/projects/ReazonSpeech/api/index.rst @@ -9,6 +9,6 @@ ReazonSpeechでは、音声処理を行うための様々なPythonインター :caption: ReazonSpeech APIリファレンス reazonspeech.nemo.asr.rst + reazonspeech.k2.asr.rst reazonspeech.espnet.asr.rst reazonspeech.espnet.oneseg.rst - reazonspeech.k2.asr.rst diff --git a/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst b/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst index e990558..1c23358 100644 --- a/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst +++ b/source/projects/ReazonSpeech/api/reazonspeech.k2.asr.rst @@ -14,7 +14,7 @@ reazonspeech.k2.asr ReazonSpeechのK2モデルをロードする。 :param device: ``cuda``, ``cpu`` または ``coreml`` - :param precision: ``fp32`` または ``int8`` + :param precision: ``fp32``, ``int8`` または ``int8-fp32`` :rtype: sherpa_onnx.OfflineRecognizer .. function:: transcribe(model, audio, config=None)