large-v3-turbo model (#2361)

README.md

@@ -57,17 +57,21 @@ pip install setuptools-rust
 
 ## Available models and languages
 
-There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model; actual speed may vary depending on many factors including the available hardware.
+There are six model sizes, four with English-only versions, offering speed and accuracy tradeoffs.
+Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model.
+The relative speeds below are measured by transcribing English speech on a A100, and the real-world speed may vary significantly depending on many factors including the language, the speaking speed, and the available hardware.
 
 |  Size  | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
 |:------:|:----------:|:------------------:|:------------------:|:-------------:|:--------------:|
-|  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~32x      |
-|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~16x      |
-| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~6x       |
+|  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~10x      |
+|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~7x       |
+| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~4x       |
 | medium |   769 M    |    `medium.en`     |      `medium`      |     ~5 GB     |      ~2x       |
 | large  |   1550 M   |        N/A         |      `large`       |    ~10 GB     |       1x       |
+| turbo  |   809 M    |        N/A         |      `turbo`       |     ~6 GB     |      ~8x       |
 
 The `.en` models for English-only applications tend to perform better, especially for the `tiny.en` and `base.en` models. We observed that the difference becomes less significant for the `small.en` and `medium.en` models.
+Additionally, the `turbo` model is an optimized version of `large-v3` that offers faster transcription speed with a minimal degradation in accuracy.
 
 Whisper's performance varies widely depending on the language. The figure below shows a performance breakdown of `large-v3` and `large-v2` models by language, using WERs (word error rates) or CER (character error rates, shown in *Italic*) evaluated on the Common Voice 15 and Fleurs datasets. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of [the paper](https://arxiv.org/abs/2212.04356), as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.
 
@@ -77,9 +81,9 @@ Whisper's performance varies widely depending on the language. The figure below
 
 ## Command-line usage
 
-The following command will transcribe speech in audio files, using the `medium` model:
+The following command will transcribe speech in audio files, using the `turbo` model:
 
-    whisper audio.flac audio.mp3 audio.wav --model medium
+    whisper audio.flac audio.mp3 audio.wav --model turbo
 
 The default setting (which selects the `small` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
 
@@ -103,7 +107,7 @@ Transcription can also be performed within Python:
 ```python
 import whisper
 
-model = whisper.load_model("base")
+model = whisper.load_model("turbo")
 result = model.transcribe("audio.mp3")
 print(result["text"])
 ```
@@ -115,7 +119,7 @@ Below is an example usage of `whisper.detect_language()` and `whisper.decode()`
 ```python
 import whisper
 
-model = whisper.load_model("base")
+model = whisper.load_model("turbo")
 
 # load audio and pad/trim it to fit 30 seconds
 audio = whisper.load_audio("audio.mp3")

model-card.md

@@ -16,13 +16,15 @@ The Whisper models are trained for speech recognition and translation tasks, cap
 | small  |   244 M    |         ✓          |         ✓          |
 | medium |   769 M    |         ✓          |         ✓          |
 | large  |   1550 M   |                    |         ✓          |
+| turbo  |   798 M    |                    |         ✓          |
 
 In December 2022, we [released an improved large model named `large-v2`](https://github.com/openai/whisper/discussions/661), and `large-v3` in November 2023.
+Additionally, we've added a `turbo` model in September 2024 which is optimized for inference speed.
 
 
 ### Release date
 
-September 2022 (original series), December 2022 (`large-v2`), and November 2023 (`large-v3`)
+September 2022 (original series), December 2022 (`large-v2`), November 2023 (`large-v3`), September 2024 (`large-v3-turbo`)
 
 ### Model type
 

whisper/__init__.py

@@ -27,6 +27,8 @@
     "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
     "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
     "large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
+    "large-v3-turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt",
+    "turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt",
 }
 
 # base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are
@@ -44,6 +46,8 @@
     "large-v2": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj",
     "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
     "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00",
+    "large-v3-turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`",
+    "turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`",
 }
 
 

whisper/transcribe.py

@@ -511,7 +511,7 @@ def valid_model_name(name):
     # fmt: off
     parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", type=valid_model_name, help="name of the Whisper model to use")
+    parser.add_argument("--model", default="turbo", type=valid_model_name, help="name of the Whisper model to use")
     parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
     parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")