diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dffc17c..84b81cc 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -41,16 +41,35 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.8', '3.9', '3.10', '3.11'] - pytorch-version: [1.13.1, 2.0.0] - exclude: - - python-version: '3.11' + include: + - python-version: '3.8' + pytorch-version: 1.10.1 + 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 + numpy-requirement: "'numpy'" + - python-version: '3.12' + pytorch-version: 2.4.1 + numpy-requirement: "'numpy'" + - python-version: '3.12' + pytorch-version: 2.5.0 + numpy-requirement: "'numpy'" steps: - uses: conda-incubator/setup-miniconda@v2 - run: conda install -n test ffmpeg python=${{ matrix.python-version }} - - run: pip3 install torch==${{ 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: pip install .["dev"] + - 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: pytest --durations=0 -vv -k 'not test_transcribe or test_transcribe[tiny] or test_transcribe[tiny.en]' -m 'not requires_cuda' diff --git a/CHANGELOG.md b/CHANGELOG.md index 5895541..7152899 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,19 @@ # CHANGELOG +## [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)) diff --git a/README.md b/README.md index afca9c9..1a661d7 100644 --- a/README.md +++ b/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. @@ -77,11 +81,11 @@ 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: +The default setting (which selects the `turbo` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option: whisper japanese.wav --language Japanese @@ -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") diff --git a/data/README.md b/data/README.md index 3b4aea1..fcb3200 100644 --- a/data/README.md +++ b/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 ad 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 and 2 of the [s5b recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5b). ## Long-form English-only datasets diff --git a/model-card.md b/model-card.md index 3c041a1..291bc4b 100644 --- a/model-card.md +++ b/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 diff --git a/requirements.txt b/requirements.txt index 62f5f9d..8ee5920 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,4 @@ torch tqdm more-itertools tiktoken -triton>=2.0.0,<3;platform_machine=="x86_64" and sys_platform=="linux" or sys_platform=="linux2" +triton>=2.0.0;platform_machine=="x86_64" and sys_platform=="linux" or sys_platform=="linux2" diff --git a/setup.py b/setup.py index 183b527..73c4eb8 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ def read_version(fname="whisper/version.py"): requirements = [] if sys.platform.startswith("linux") and platform.machine() == "x86_64": - requirements.append("triton>=2.0.0,<3") + requirements.append("triton>=2.0.0") setup( name="openai-whisper", diff --git a/whisper/__init__.py b/whisper/__init__.py index d7fbba3..e210718 100644 --- a/whisper/__init__.py +++ b/whisper/__init__.py @@ -27,6 +27,8 @@ _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 @@ -44,6 +46,8 @@ _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`", } diff --git a/whisper/model.py b/whisper/model.py index a678283..e537447 100644 --- a/whisper/model.py +++ b/whisper/model.py @@ -1,7 +1,8 @@ import base64 import gzip +from contextlib import contextmanager from dataclasses import dataclass -from typing import Dict, Iterable, Optional +from typing import Dict, Iterable, Optional, Tuple import numpy as np import torch @@ -12,6 +13,14 @@ 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: @@ -59,7 +68,19 @@ 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 @@ -92,20 +113,30 @@ 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) * scale - k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) - qk = q @ k - if mask is not None: - qk = qk + mask[:n_ctx, :n_ctx] - qk = qk.float() + if SDPA_AVAILABLE and MultiHeadAttention.use_sdpa: + 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) - return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + w = F.softmax(qk, dim=-1).to(q.dtype) + out = (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) + qk = qk.detach() + + return out, qk class ResidualAttentionBlock(nn.Module): diff --git a/whisper/timing.py b/whisper/timing.py index b695ead..e563414 100644 --- a/whisper/timing.py +++ b/whisper/timing.py @@ -191,7 +191,9 @@ def find_alignment( for i, block in enumerate(model.decoder.blocks) ] - with torch.no_grad(): + from .model import disable_sdpa + + 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) diff --git a/whisper/transcribe.py b/whisper/transcribe.py index 911f36c..0a4cc36 100644 --- a/whisper/transcribe.py +++ b/whisper/transcribe.py @@ -46,6 +46,7 @@ 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 = "\"'.。,，!！?？:：”)]}、", @@ -102,6 +103,11 @@ 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 @@ -229,9 +235,11 @@ 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 = [] @@ -277,7 +285,13 @@ def transcribe( segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) - decode_options["prompt"] = all_tokens[prompt_reset_since:] + 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) @@ -513,7 +527,7 @@ 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="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") @@ -531,6 +545,8 @@ 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") diff --git a/whisper/version.py b/whisper/version.py index c96dd9c..b4b3350 100644 --- a/whisper/version.py +++ b/whisper/version.py @@ -1 +1 @@ -__version__ = "20231117" +__version__ = "20240930"