.github/dependabot.yml

@@ -1,13 +0,0 @@
-# Keep GitHub Actions up to date with GitHub's Dependabot...
-# https://docs.github.com/en/code-security/dependabot/working-with-dependabot/keeping-your-actions-up-to-date-with-dependabot
-# https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file#package-ecosystem
-version: 2
-updates:
-  - package-ecosystem: github-actions
-    directory: /
-    groups:
-      github-actions:
-        patterns:
-          - "*"  # Group all Actions updates into a single larger pull request
-    schedule:
-      interval: weekly

.github/workflows/python-publish.yml

@@ -8,23 +8,23 @@ jobs:
   deploy:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
     - uses: actions-ecosystem/action-regex-match@v2
       id: regex-match
       with:
         text: ${{ github.event.head_commit.message }}
         regex: '^Release ([^ ]+)'
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v4
       with:
-        python-version: '3.12'
+        python-version: '3.8'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install setuptools wheel twine build
+        pip install setuptools wheel twine
     - name: Release
       if: ${{ steps.regex-match.outputs.match != '' }}
-      uses: softprops/action-gh-release@v2
+      uses: softprops/action-gh-release@v1
       with:
         tag_name: v${{ steps.regex-match.outputs.group1 }}
     - name: Build and publish
@@ -33,5 +33,5 @@ jobs:
         TWINE_USERNAME: __token__
         TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
       run: |
-        python -m build --sdist
+        python setup.py sdist
         twine upload dist/*

.github/workflows/test.yml

@@ -11,19 +11,19 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - name: Fetch base branch
         run: git fetch origin ${{ github.base_ref }}
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v4
         with:
-          python-version: "3.9"
+          python-version: "3.8"
           architecture: x64
       - name: Get pip cache dir
         id: pip-cache
         run: |
           echo "dir=$(pip cache dir)" >> $GITHUB_OUTPUT
       - name: pip/pre-commit cache
-        uses: actions/cache@v4
+        uses: actions/cache@v3
         with:
           path: |
             ${{ steps.pip-cache.outputs.dir }}
@@ -33,47 +33,24 @@ jobs:
             ${{ runner.os }}-pip-pre-commit
       - name: pre-commit
         run: |
-          pip install --upgrade pre-commit
+          pip install -U pre-commit
           pre-commit install --install-hooks
           pre-commit run --all-files
   whisper-test:
     needs: pre-commit
     runs-on: ubuntu-latest
     strategy:
-      fail-fast: false
       matrix:
-        include:
+        python-version: ['3.8', '3.9', '3.10', '3.11']
-          - python-version: '3.8'
+        pytorch-version: [1.13.1, 2.0.0]
-            pytorch-version: 1.10.1
+        exclude:
-            numpy-requirement: "'numpy<2'"
-          - python-version: '3.8'
-            pytorch-version: 1.13.1
-            numpy-requirement: "'numpy<2'"
-          - python-version: '3.8'
-            pytorch-version: 2.0.1
-            numpy-requirement: "'numpy<2'"
-          - python-version: '3.9'
-            pytorch-version: 2.1.2
-            numpy-requirement: "'numpy<2'"
-          - python-version: '3.10'
-            pytorch-version: 2.2.2
-            numpy-requirement: "'numpy<2'"
           - python-version: '3.11'
-            pytorch-version: 2.3.1
+            pytorch-version: 1.13.1
-            numpy-requirement: "'numpy'"
-          - python-version: '3.12'
-            pytorch-version: 2.4.1
-            numpy-requirement: "'numpy'"
-          - python-version: '3.12'
-            pytorch-version: 2.5.1
-            numpy-requirement: "'numpy'"
-          - python-version: '3.13'
-            pytorch-version: 2.5.1
-            numpy-requirement: "'numpy'"
     steps:
-      - uses: conda-incubator/setup-miniconda@v3
+      - uses: conda-incubator/setup-miniconda@v2
       - run: conda install -n test ffmpeg python=${{ matrix.python-version }}
-      - uses: actions/checkout@v4
+      - run: pip3 install torch==${{ matrix.pytorch-version }}+cpu --index-url https://download.pytorch.org/whl/cpu
+      - uses: actions/checkout@v3
       - run: echo "$CONDA/envs/test/bin" >> $GITHUB_PATH
-      - run: pip3 install .["dev"] ${{ matrix.numpy-requirement }} torch==${{ matrix.pytorch-version }}+cpu --index-url https://download.pytorch.org/whl/cpu --extra-index-url https://pypi.org/simple
+      - run: pip install .["dev"]
       - run: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' -m 'not requires_cuda'

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v4.0.1
     hooks:
       - id: check-json
       - id: end-of-file-fixer
@@ -11,17 +11,17 @@ repos:
       - id: check-added-large-files
         args: [--maxkb=4096]
   - repo: https://github.com/psf/black
-    rev: 25.1.0
+    rev: 23.7.0
     hooks:
       - id: black
   - repo: https://github.com/pycqa/isort
-    rev: 6.0.0
+    rev: 5.12.0
     hooks:
       - id: isort
         name: isort (python)
         args: ["--profile", "black", "-l", "88", "--trailing-comma", "--multi-line", "3"]
   - repo: https://github.com/pycqa/flake8.git
-    rev: 7.1.1
+    rev: 6.0.0
     hooks:
       - id: flake8
         types: [python]

CHANGELOG.md

@@ -1,40 +1,5 @@
 # CHANGELOG
 
-## [v20250625](https://github.com/openai/whisper/releases/tag/v20250625)
-
-* Fix: Update torch.load to use weights_only=True to prevent security w… ([#2451](https://github.com/openai/whisper/pull/2451))
-* Fix: Ensure DTW cost tensor is on the same device as input tensor ([#2561](https://github.com/openai/whisper/pull/2561))
-* docs: updated README to specify translation model limitation ([#2547](https://github.com/openai/whisper/pull/2547))
-* Fixed triton kernel update to support latest triton versions ([#2588](https://github.com/openai/whisper/pull/2588))
-* Fix: GitHub display errors for Jupyter notebooks ([#2589](https://github.com/openai/whisper/pull/2589))
-* Bump the github-actions group with 3 updates ([#2592](https://github.com/openai/whisper/pull/2592))
-* Keep GitHub Actions up to date with GitHub's Dependabot ([#2486](https://github.com/openai/whisper/pull/2486))
-* pre-commit: Upgrade black v25.1.0 and isort v6.0.0 ([#2514](https://github.com/openai/whisper/pull/2514))
-* GitHub Actions: Add Python 3.13 to the testing ([#2487](https://github.com/openai/whisper/pull/2487))
-* PEP 621: Migrate from setup.py to pyproject.toml ([#2435](https://github.com/openai/whisper/pull/2435))
-* pre-commit autoupdate && pre-commit run --all-files ([#2484](https://github.com/openai/whisper/pull/2484))
-* Upgrade GitHub Actions ([#2430](https://github.com/openai/whisper/pull/2430))
-* Bugfix: Illogical "Avoid computing higher temperatures on no_speech" ([#1903](https://github.com/openai/whisper/pull/1903))
-* Updating README and doc strings to reflect that n_mels can now be 128 ([#2049](https://github.com/openai/whisper/pull/2049))
-* fix typo data/README.md ([#2433](https://github.com/openai/whisper/pull/2433))
-* Update README.md ([#2379](https://github.com/openai/whisper/pull/2379))
-* Add option to carry initial_prompt with the sliding window ([#2343](https://github.com/openai/whisper/pull/2343))
-* more pytorch versions in tests ([#2408](https://github.com/openai/whisper/pull/2408))
-
-## [v20240930](https://github.com/openai/whisper/releases/tag/v20240930)
-
-* allowing numpy 2 in tests ([#2362](https://github.com/openai/whisper/pull/2362))
-* large-v3-turbo model ([#2361](https://github.com/openai/whisper/pull/2361))
-* test on python/pytorch versions up to 3.12 and 2.4.1 ([#2360](https://github.com/openai/whisper/pull/2360))
-* using sdpa if available ([#2359](https://github.com/openai/whisper/pull/2359))
-
-## [v20240927](https://github.com/openai/whisper/releases/tag/v20240927)
-
-* pinning numpy<2 in tests ([#2332](https://github.com/openai/whisper/pull/2332))
-* Relax triton requirements for compatibility with pytorch 2.4 and newer ([#2307](https://github.com/openai/whisper/pull/2307))
-* Skip silence around hallucinations ([#1838](https://github.com/openai/whisper/pull/1838))
-* Fix triton env marker ([#1887](https://github.com/openai/whisper/pull/1887))
-
 ## [v20231117](https://github.com/openai/whisper/releases/tag/v20231117)
 
 * Relax triton requirements for compatibility with pytorch 2.1 and newer ([#1802](https://github.com/openai/whisper/pull/1802))

README.md

@@ -57,55 +57,41 @@ pip install setuptools-rust
 
 ## Available models and languages
 
-There are six model sizes, four with English-only versions, offering speed and accuracy tradeoffs.
+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.
-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     |      ~10x      |
+|  tiny  |    39 M    |     `tiny.en`      |       `tiny`       |     ~1 GB     |      ~32x      |
-|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~7x       |
+|  base  |    74 M    |     `base.en`      |       `base`       |     ~1 GB     |      ~16x      |
-| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~4x       |
+| small  |   244 M    |     `small.en`     |      `small`       |     ~2 GB     |      ~6x       |
 | 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.
 
 ![WER breakdown by language](https://github.com/openai/whisper/assets/266841/f4619d66-1058-4005-8f67-a9d811b77c62)
 
+
+
 ## Command-line usage
 
-The following command will transcribe speech in audio files, using the `turbo` model:
+The following command will transcribe speech in audio files, using the `medium` model:
 
-```bash
+    whisper audio.flac audio.mp3 audio.wav --model medium
-whisper audio.flac audio.mp3 audio.wav --model turbo
-```
 
-The default setting (which selects the `turbo` model) works well for transcribing English. However, **the `turbo` model is not trained for translation tasks**. If you need to **translate non-English speech into English**, use one of the **multilingual models** (`tiny`, `base`, `small`, `medium`, `large`) instead of `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:
 
-For example, to transcribe an audio file containing non-English speech, you can specify the language:
+    whisper japanese.wav --language Japanese
 
-```bash
+Adding `--task translate` will translate the speech into English:
-whisper japanese.wav --language Japanese
-```
 
-To **translate** speech into English, use:
+    whisper japanese.wav --language Japanese --task translate
-
-```bash
-whisper japanese.wav --model medium --language Japanese --task translate
-```
-
-> **Note:** The `turbo` model will return the original language even if `--task translate` is specified. Use `medium` or `large` for the best translation results.
 
 Run the following to view all available options:
 
-```bash
+    whisper --help
-whisper --help
-```
 
 See [tokenizer.py](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py) for the list of all available languages.
 
@@ -117,7 +103,7 @@ Transcription can also be performed within Python:
 ```python
 import whisper
 
-model = whisper.load_model("turbo")
+model = whisper.load_model("base")
 result = model.transcribe("audio.mp3")
 print(result["text"])
 ```
@@ -129,14 +115,14 @@ Below is an example usage of `whisper.detect_language()` and `whisper.decode()`
 ```python
 import whisper
 
-model = whisper.load_model("turbo")
+model = whisper.load_model("base")
 
 # load audio and pad/trim it to fit 30 seconds
 audio = whisper.load_audio("audio.mp3")
 audio = whisper.pad_or_trim(audio)
 
 # make log-Mel spectrogram and move to the same device as the model
-mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
+mel = whisper.log_mel_spectrogram(audio).to(model.device)
 
 # detect the spoken language
 _, probs = model.detect_language(mel)

data/README.md

@@ -45,7 +45,7 @@ We downloaded the [CHiME-5 dataset](https://spandh.dcs.shef.ac.uk//chime_challen
 
 ### AMI-IHM, AMI-SDM1
 
-We preprocessed the [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) by following the stage 0 and 2 of the [s5b recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5b).
+We preprocessed the [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) by following the stage 0 ad 2 of the [s5b recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5b).
 
 
 ## Long-form English-only datasets

model-card.md

@@ -16,15 +16,13 @@ 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`), November 2023 (`large-v3`), September 2024 (`large-v3-turbo`)
+September 2022 (original series), December 2022 (`large-v2`), and November 2023 (`large-v3`)
 
 ### Model type
 

notebooks/LibriSpeech.ipynb

@@ -949,8 +949,7 @@
       "style": "IPY_MODEL_039b53f2702c4179af7e0548018d0588",
       "value": " 164/164 [05:08<00:00,  1.86s/it]"
      }
-    },
+    }
-    "state": {}
    }
   }
  },

notebooks/Multilingual_ASR.ipynb

@@ -4219,8 +4219,7 @@
             "_view_name": "StyleView",
             "description_width": ""
           }
-        },
+        }
-        "state": {}
       }
     }
   },

pyproject.toml

@@ -1,50 +1,3 @@
-[build-system]
-build-backend = "setuptools.build_meta"
-
-requires = [ "setuptools>=61.2" ]
-
-[project]
-name = "openai-whisper"
-description = "Robust Speech Recognition via Large-Scale Weak Supervision"
-readme.content-type = "text/markdown"
-readme.file = "README.md"
-license = { text = "MIT" }
-authors = [ { name = "OpenAI" } ]
-requires-python = ">=3.8"
-classifiers = [
-  "Programming Language :: Python :: 3 :: Only",
-  "Programming Language :: Python :: 3.8",
-  "Programming Language :: Python :: 3.9",
-  "Programming Language :: Python :: 3.10",
-  "Programming Language :: Python :: 3.11",
-  "Programming Language :: Python :: 3.12",
-  "Programming Language :: Python :: 3.13",
-]
-dynamic = [ "version" ]
-dependencies = [
-  "more-itertools",
-  "numba",
-  "numpy",
-  "tiktoken",
-  "torch",
-  "tqdm",
-  "triton>=2; (platform_machine=='x86_64' and sys_platform=='linux') or sys_platform=='linux2'",
-]
-optional-dependencies.dev = [ "black", "flake8", "isort", "pytest", "scipy" ]
-urls = { Homepage = "https://github.com/openai/whisper" }
-scripts.whisper = "whisper.transcribe:cli"
-
-[tool.setuptools]
-py-modules = [ "whisper" ]
-include-package-data = true
-
-[tool.setuptools.dynamic]
-version = { attr = "whisper.version.__version__" }
-
-[tool.setuptools.packages.find]
-exclude = [ "tests*" ]
-namespaces = false
-
 [tool.black]
 
 [tool.isort]
@@ -52,3 +5,4 @@ profile = "black"
 include_trailing_comma = true
 line_length = 88
 multi_line_output = 3
+

requirements.txt

@@ -4,4 +4,3 @@ torch
 tqdm
 more-itertools
 tiktoken
-triton>=2.0.0;platform_machine=="x86_64" and sys_platform=="linux" or sys_platform=="linux2"

setup.py

@@ -0,0 +1,43 @@
+import os
+import platform
+import sys
+
+import pkg_resources
+from setuptools import find_packages, setup
+
+
+def read_version(fname="whisper/version.py"):
+    exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
+    return locals()["__version__"]
+
+
+requirements = []
+if sys.platform.startswith("linux") and platform.machine() == "x86_64":
+    requirements.append("triton>=2.0.0,<3")
+
+setup(
+    name="openai-whisper",
+    py_modules=["whisper"],
+    version=read_version(),
+    description="Robust Speech Recognition via Large-Scale Weak Supervision",
+    long_description=open("README.md", encoding="utf-8").read(),
+    long_description_content_type="text/markdown",
+    readme="README.md",
+    python_requires=">=3.8",
+    author="OpenAI",
+    url="https://github.com/openai/whisper",
+    license="MIT",
+    packages=find_packages(exclude=["tests*"]),
+    install_requires=requirements
+    + [
+        str(r)
+        for r in pkg_resources.parse_requirements(
+            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
+        )
+    ],
+    entry_points={
+        "console_scripts": ["whisper=whisper.transcribe:cli"],
+    },
+    include_package_data=True,
+    extras_require={"dev": ["pytest", "scipy", "black", "flake8", "isort"]},
+)

whisper/__init__.py

@@ -27,8 +27,6 @@ _MODELS = {
     "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
@@ -46,8 +44,6 @@ _ALIGNMENT_HEADS = {
     "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`",
 }
 
 
@@ -147,8 +143,7 @@ def load_model(
     with (
         io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
     ) as fp:
-        kwargs = {"weights_only": True} if torch.__version__ >= "1.13" else {}
+        checkpoint = torch.load(fp, map_location=device)
-        checkpoint = torch.load(fp, map_location=device, **kwargs)
     del checkpoint_file
 
     dims = ModelDimensions(**checkpoint["dims"])

whisper/audio.py

@@ -122,7 +122,7 @@ def log_mel_spectrogram(
         The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
 
     n_mels: int
-        The number of Mel-frequency filters, only 80 and 128 are supported
+        The number of Mel-frequency filters, only 80 is supported
 
     padding: int
         Number of zero samples to pad to the right
@@ -132,7 +132,7 @@ def log_mel_spectrogram(
 
     Returns
     -------
-    torch.Tensor, shape = (n_mels, n_frames)
+    torch.Tensor, shape = (80, n_frames)
         A Tensor that contains the Mel spectrogram
     """
     if not torch.is_tensor(audio):

whisper/model.py

@@ -1,8 +1,7 @@
 import base64
 import gzip
-from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import Dict, Iterable, Optional, Tuple
+from typing import Dict, Iterable, Optional
 
 import numpy as np
 import torch
@@ -13,14 +12,6 @@ from .decoding import decode as decode_function
 from .decoding import detect_language as detect_language_function
 from .transcribe import transcribe as transcribe_function
 
-try:
-    from torch.nn.functional import scaled_dot_product_attention
-
-    SDPA_AVAILABLE = True
-except (ImportError, RuntimeError, OSError):
-    scaled_dot_product_attention = None
-    SDPA_AVAILABLE = False
-
 
 @dataclass
 class ModelDimensions:
@@ -68,19 +59,7 @@ def sinusoids(length, channels, max_timescale=10000):
     return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
 
 
-@contextmanager
-def disable_sdpa():
-    prev_state = MultiHeadAttention.use_sdpa
-    try:
-        MultiHeadAttention.use_sdpa = False
-        yield
-    finally:
-        MultiHeadAttention.use_sdpa = prev_state
-
-
 class MultiHeadAttention(nn.Module):
-    use_sdpa = True
-
     def __init__(self, n_state: int, n_head: int):
         super().__init__()
         self.n_head = n_head
@@ -113,30 +92,20 @@ class MultiHeadAttention(nn.Module):
 
     def qkv_attention(
         self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    ):
         n_batch, n_ctx, n_state = q.shape
         scale = (n_state // self.n_head) ** -0.25
-        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
-        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
         v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
 
-        if SDPA_AVAILABLE and MultiHeadAttention.use_sdpa:
+        qk = q @ k
-            a = scaled_dot_product_attention(
-                q, k, v, is_causal=mask is not None and n_ctx > 1
-            )
-            out = a.permute(0, 2, 1, 3).flatten(start_dim=2)
-            qk = None
-        else:
-            qk = (q * scale) @ (k * scale).transpose(-1, -2)
         if mask is not None:
             qk = qk + mask[:n_ctx, :n_ctx]
         qk = qk.float()
 
         w = F.softmax(qk, dim=-1).to(q.dtype)
-            out = (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
-            qk = qk.detach()
-
-        return out, qk
 
 
 class ResidualAttentionBlock(nn.Module):

whisper/normalizers/basic.py

@@ -30,19 +30,15 @@ def remove_symbols_and_diacritics(s: str, keep=""):
     and drop any diacritics (category 'Mn' and some manual mappings)
     """
     return "".join(
-        (
         c
         if c in keep
-            else (
+        else ADDITIONAL_DIACRITICS[c]
-                ADDITIONAL_DIACRITICS[c]
         if c in ADDITIONAL_DIACRITICS
-                else (
+        else ""
-                    ""
         if unicodedata.category(c) == "Mn"
-                    else " " if unicodedata.category(c)[0] in "MSP" else c
+        else " "
-                )
+        if unicodedata.category(c)[0] in "MSP"
-            )
+        else c
-        )
         for c in unicodedata.normalize("NFKD", s)
     )
 

whisper/timing.py

@@ -117,7 +117,7 @@ def dtw_cuda(x, BLOCK_SIZE=1024):
     x_skew = x_skew.T.contiguous()
     cost = torch.ones(N + M + 2, M + 2) * np.inf
     cost[0, 0] = 0
-    cost = cost.to(x.device)
+    cost = cost.cuda()
     trace = torch.zeros_like(cost, dtype=torch.int32)
 
     dtw_kernel[(1,)](
@@ -191,9 +191,7 @@ def find_alignment(
         for i, block in enumerate(model.decoder.blocks)
     ]
 
-    from .model import disable_sdpa
+    with torch.no_grad():
-
-    with torch.no_grad(), disable_sdpa():
         logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0]
         sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot]
         token_probs = sampled_logits.softmax(dim=-1)
@@ -301,7 +299,6 @@ def add_word_timestamps(
     word_durations = np.array([t.end - t.start for t in alignment])
     word_durations = word_durations[word_durations.nonzero()]
     median_duration = np.median(word_durations) if len(word_durations) > 0 else 0.0
-    median_duration = min(0.7, float(median_duration))
     max_duration = median_duration * 2
 
     # hack: truncate long words at sentence boundaries.

whisper/transcribe.py

@@ -2,7 +2,7 @@ import argparse
 import os
 import traceback
 import warnings
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -23,7 +23,6 @@ from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
 from .utils import (
     exact_div,
     format_timestamp,
-    get_end,
     get_writer,
     make_safe,
     optional_float,
@@ -46,12 +45,9 @@ def transcribe(
     no_speech_threshold: Optional[float] = 0.6,
     condition_on_previous_text: bool = True,
     initial_prompt: Optional[str] = None,
-    carry_initial_prompt: bool = False,
     word_timestamps: bool = False,
     prepend_punctuations: str = "\"'“¿([{-",
     append_punctuations: str = "\"'.。,，!！?？:：”)]}、",
-    clip_timestamps: Union[str, List[float]] = "0",
-    hallucination_silence_threshold: Optional[float] = None,
     **decode_options,
 ):
     """
@@ -103,22 +99,9 @@ def transcribe(
         "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
         to make it more likely to predict those word correctly.
 
-    carry_initial_prompt: bool
-        If carry_initial_prompt is True, `initial_prompt` is prepended to the prompt of each internal
-        `decode()` call. If there is not enough context space at the start of the prompt, it is
-        left-sliced to make space.
-
     decode_options: dict
         Keyword arguments to construct `DecodingOptions` instances
 
-    clip_timestamps: Union[str, List[float]]
-        Comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process.
-        The last end timestamp defaults to the end of the file.
-
-    hallucination_silence_threshold: Optional[float]
-        When word_timestamps is True, skip silent periods longer than this threshold (in seconds)
-        when a possible hallucination is detected
-
     Returns
     -------
     A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
@@ -138,7 +121,6 @@ def transcribe(
     # Pad 30-seconds of silence to the input audio, for slicing
     mel = log_mel_spectrogram(audio, model.dims.n_mels, padding=N_SAMPLES)
     content_frames = mel.shape[-1] - N_FRAMES
-    content_duration = float(content_frames * HOP_LENGTH / SAMPLE_RATE)
 
     if decode_options.get("language", None) is None:
         if not model.is_multilingual:
@@ -165,19 +147,6 @@ def transcribe(
         task=task,
     )
 
-    if isinstance(clip_timestamps, str):
-        clip_timestamps = [
-            float(ts) for ts in (clip_timestamps.split(",") if clip_timestamps else [])
-        ]
-    seek_points: List[int] = [round(ts * FRAMES_PER_SECOND) for ts in clip_timestamps]
-    if len(seek_points) == 0:
-        seek_points.append(0)
-    if len(seek_points) % 2 == 1:
-        seek_points.append(content_frames)
-    seek_clips: List[Tuple[int, int]] = list(zip(seek_points[::2], seek_points[1::2]))
-
-    punctuation = "\"'“¿([{-\"'.。,，!！?？:：”)]}、"
-
     if word_timestamps and task == "translate":
         warnings.warn("Word-level timestamps on translations may not be reliable.")
 
@@ -214,8 +183,6 @@ def transcribe(
             if (
                 no_speech_threshold is not None
                 and decode_result.no_speech_prob > no_speech_threshold
-                and logprob_threshold is not None
-                and decode_result.avg_logprob < logprob_threshold
             ):
                 needs_fallback = False  # silence
             if not needs_fallback:
@@ -223,8 +190,7 @@ def transcribe(
 
         return decode_result
 
-    clip_idx = 0
+    seek = 0
-    seek = seek_clips[clip_idx][0]
     input_stride = exact_div(
         N_FRAMES, model.dims.n_audio_ctx
     )  # mel frames per output token: 2
@@ -235,11 +201,9 @@ def transcribe(
     all_segments = []
     prompt_reset_since = 0
 
-    remaining_prompt_length = model.dims.n_text_ctx // 2 - 1
     if initial_prompt is not None:
         initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
         all_tokens.extend(initial_prompt_tokens)
-        remaining_prompt_length -= len(initial_prompt_tokens)
     else:
         initial_prompt_tokens = []
 
@@ -265,33 +229,14 @@ def transcribe(
         total=content_frames, unit="frames", disable=verbose is not False
     ) as pbar:
         last_speech_timestamp = 0.0
-        # NOTE: This loop is obscurely flattened to make the diff readable.
+        while seek < content_frames:
-        # A later commit should turn this into a simpler nested loop.
-        # for seek_clip_start, seek_clip_end in seek_clips:
-        #     while seek < seek_clip_end
-        while clip_idx < len(seek_clips):
-            seek_clip_start, seek_clip_end = seek_clips[clip_idx]
-            if seek < seek_clip_start:
-                seek = seek_clip_start
-            if seek >= seek_clip_end:
-                clip_idx += 1
-                if clip_idx < len(seek_clips):
-                    seek = seek_clips[clip_idx][0]
-                continue
             time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE)
-            window_end_time = float((seek + N_FRAMES) * HOP_LENGTH / SAMPLE_RATE)
+            mel_segment = mel[:, seek : seek + N_FRAMES]
-            segment_size = min(N_FRAMES, content_frames - seek, seek_clip_end - seek)
+            segment_size = min(N_FRAMES, content_frames - seek)
-            mel_segment = mel[:, seek : seek + segment_size]
             segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
             mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
 
-            if carry_initial_prompt:
-                nignored = max(len(initial_prompt_tokens), prompt_reset_since)
-                remaining_prompt = all_tokens[nignored:][-remaining_prompt_length:]
-                decode_options["prompt"] = initial_prompt_tokens + remaining_prompt
-            else:
             decode_options["prompt"] = all_tokens[prompt_reset_since:]
-
             result: DecodingResult = decode_with_fallback(mel_segment)
             tokens = torch.tensor(result.tokens)
 
@@ -312,30 +257,6 @@ def transcribe(
             previous_seek = seek
             current_segments = []
 
-            # anomalous words are very long/short/improbable
-            def word_anomaly_score(word: dict) -> float:
-                probability = word.get("probability", 0.0)
-                duration = word["end"] - word["start"]
-                score = 0.0
-                if probability < 0.15:
-                    score += 1.0
-                if duration < 0.133:
-                    score += (0.133 - duration) * 15
-                if duration > 2.0:
-                    score += duration - 2.0
-                return score
-
-            def is_segment_anomaly(segment: Optional[dict]) -> bool:
-                if segment is None or not segment["words"]:
-                    return False
-                words = [w for w in segment["words"] if w["word"] not in punctuation]
-                words = words[:8]
-                score = sum(word_anomaly_score(w) for w in words)
-                return score >= 3 or score + 0.01 >= len(words)
-
-            def next_words_segment(segments: List[dict]) -> Optional[dict]:
-                return next((s for s in segments if s["words"]), None)
-
             timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin)
             single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True]
 
@@ -409,71 +330,17 @@ def transcribe(
                     append_punctuations=append_punctuations,
                     last_speech_timestamp=last_speech_timestamp,
                 )
-
+                word_end_timestamps = [
-                if not single_timestamp_ending:
+                    w["end"] for s in current_segments for w in s["words"]
-                    last_word_end = get_end(current_segments)
+                ]
-                    if last_word_end is not None and last_word_end > time_offset:
+                if len(word_end_timestamps) > 0:
-                        seek = round(last_word_end * FRAMES_PER_SECOND)
+                    last_speech_timestamp = word_end_timestamps[-1]
-
+                if not single_timestamp_ending and len(word_end_timestamps) > 0:
-                # skip silence before possible hallucinations
+                    seek_shift = round(
-                if hallucination_silence_threshold is not None:
+                        (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND
-                    threshold = hallucination_silence_threshold
-                    if not single_timestamp_ending:
-                        last_word_end = get_end(current_segments)
-                        if last_word_end is not None and last_word_end > time_offset:
-                            remaining_duration = window_end_time - last_word_end
-                            if remaining_duration > threshold:
-                                seek = round(last_word_end * FRAMES_PER_SECOND)
-                            else:
-                                seek = previous_seek + segment_size
-
-                    # if first segment might be a hallucination, skip leading silence
-                    first_segment = next_words_segment(current_segments)
-                    if first_segment is not None and is_segment_anomaly(first_segment):
-                        gap = first_segment["start"] - time_offset
-                        if gap > threshold:
-                            seek = previous_seek + round(gap * FRAMES_PER_SECOND)
-                            continue
-
-                    # skip silence before any possible hallucination that is surrounded
-                    # by silence or more hallucinations
-                    hal_last_end = last_speech_timestamp
-                    for si in range(len(current_segments)):
-                        segment = current_segments[si]
-                        if not segment["words"]:
-                            continue
-                        if is_segment_anomaly(segment):
-                            next_segment = next_words_segment(
-                                current_segments[si + 1 :]
                     )
-                            if next_segment is not None:
+                    if seek_shift > 0:
-                                hal_next_start = next_segment["words"][0]["start"]
+                        seek = previous_seek + seek_shift
-                            else:
-                                hal_next_start = time_offset + segment_duration
-                            silence_before = (
-                                segment["start"] - hal_last_end > threshold
-                                or segment["start"] < threshold
-                                or segment["start"] - time_offset < 2.0
-                            )
-                            silence_after = (
-                                hal_next_start - segment["end"] > threshold
-                                or is_segment_anomaly(next_segment)
-                                or window_end_time - segment["end"] < 2.0
-                            )
-                            if silence_before and silence_after:
-                                seek = round(
-                                    max(time_offset + 1, segment["start"])
-                                    * FRAMES_PER_SECOND
-                                )
-                                if content_duration - segment["end"] < threshold:
-                                    seek = content_frames
-                                current_segments[si:] = []
-                                break
-                        hal_last_end = segment["end"]
-
-                last_word_end = get_end(current_segments)
-                if last_word_end is not None:
-                    last_speech_timestamp = last_word_end
 
             if verbose:
                 for segment in current_segments:
@@ -527,7 +394,7 @@ def cli():
     # 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="turbo", type=valid_model_name, help="name of the Whisper model to use")
+    parser.add_argument("--model", default="small", 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")
@@ -545,8 +412,6 @@ def cli():
 
     parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
     parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--carry_initial_prompt", type=str2bool, default=False, help="if True, prepend initial_prompt to every internal decode() call. May reduce the effectiveness of condition_on_previous_text")
-
     parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
     parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
 
@@ -562,8 +427,6 @@ def cli():
     parser.add_argument("--max_line_count", type=optional_int, default=None, help="(requires --word_timestamps True) the maximum number of lines in a segment")
     parser.add_argument("--max_words_per_line", type=optional_int, default=None, help="(requires --word_timestamps True, no effect with --max_line_width) the maximum number of words in a segment")
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
-    parser.add_argument("--clip_timestamps", type=str, default="0", help="comma-separated list start,end,start,end,... timestamps (in seconds) of clips to process, where the last end timestamp defaults to the end of the file")
-    parser.add_argument("--hallucination_silence_threshold", type=optional_float, help="(requires --word_timestamps True) skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected")
     # fmt: on
 
     args = parser.parse_args().__dict__

whisper/triton_ops.py

@@ -60,7 +60,7 @@ def median_kernel(filter_width: int):
         tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask)  # noqa: F821
 
     kernel = triton.JITFunction(kernel.fn)
-    new_kernel = kernel.src.replace(
+    kernel.src = kernel.src.replace(
         "    LOAD_ALL_ROWS_HERE",
         "\n".join(
             [
@@ -69,8 +69,7 @@ def median_kernel(filter_width: int):
             ]
         ),
     )
-
+    kernel.src = kernel.src.replace(
-    new_kernel = new_kernel.replace(
         "    BUBBLESORT_HERE",
         "\n\n".join(
             [
@@ -91,14 +90,7 @@ def median_kernel(filter_width: int):
             ]
         ),
     )
-
+    kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}")
-    new_kernel = new_kernel.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}")
-
-    if hasattr(kernel, "_unsafe_update_src") is True:
-        kernel._unsafe_update_src(new_kernel)
-        kernel.hash = None
-    else:
-        kernel.src = new_kernel
 
     return kernel
 

whisper/utils.py

@@ -3,7 +3,7 @@ import os
 import re
 import sys
 import zlib
-from typing import Callable, List, Optional, TextIO
+from typing import Callable, Optional, TextIO
 
 system_encoding = sys.getdefaultencoding()
 
@@ -68,20 +68,6 @@ def format_timestamp(
     )
 
 
-def get_start(segments: List[dict]) -> Optional[float]:
-    return next(
-        (w["start"] for s in segments for w in s["words"]),
-        segments[0]["start"] if segments else None,
-    )
-
-
-def get_end(segments: List[dict]) -> Optional[float]:
-    return next(
-        (w["end"] for s in reversed(segments) for w in reversed(s["words"])),
-        segments[-1]["end"] if segments else None,
-    )
-
-
 class ResultWriter:
     extension: str
 
@@ -143,8 +129,8 @@ class SubtitlesWriter(ResultWriter):
             line_len = 0
             line_count = 1
             # the next subtitle to yield (a list of word timings with whitespace)
-            subtitle: List[dict] = []
+            subtitle: list[dict] = []
-            last: float = get_start(result["segments"]) or 0.0
+            last = result["segments"][0]["words"][0]["start"]
             for segment in result["segments"]:
                 chunk_index = 0
                 words_count = max_words_per_line
@@ -209,11 +195,9 @@ class SubtitlesWriter(ResultWriter):
 
                         yield start, end, "".join(
                             [
-                                (
                                 re.sub(r"^(\s*)(.*)$", r"\1<u>\2</u>", word)
                                 if j == i
                                 else word
-                                )
                                 for j, word in enumerate(all_words)
                             ]
                         )

whisper/version.py

@@ -1 +1 @@
-__version__ = "20250625"
+__version__ = "20231117"