mirror of
https://github.com/ggerganov/whisper.cpp.git
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whisper : add support for large v3 (#1444)
* whisper : add support for large v3 * bench : fix build + fix go bindings * bench : fix n_mels * models : update readme
This commit is contained in:
Georgi Gerganov
GitHub
parent
973111088b
commit
2cdfc4e025
3
Makefile
3
Makefile
@ -417,9 +417,10 @@ samples:
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.PHONY: medium.en
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.PHONY: medium
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.PHONY: large-v1
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.PHONY: large-v2
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.PHONY: large
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tiny.en tiny base.en base small.en small medium.en medium large-v1 large: main
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tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large: main
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bash ./models/download-ggml-model.sh $@
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@echo ""
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@echo "==============================================="
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@ -234,6 +234,7 @@ make small
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make medium.en
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make medium
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make large-v1
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make large-v2
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make large
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```
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@ -245,7 +246,7 @@ make large
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| base | 142 MB | ~210 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
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| small | 466 MB | ~600 MB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
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| medium | 1.5 GB | ~1.7 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
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| large | 2.9 GB | ~3.3 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
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| large | 2.9 GB | ~3.3 GB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
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## Quantization
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@ -24,7 +24,7 @@ const (
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var (
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// The models which will be downloaded, if no model is specified as an argument
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modelNames = []string{"ggml-tiny.en", "ggml-tiny", "ggml-base.en", "ggml-base", "ggml-small.en", "ggml-small", "ggml-medium.en", "ggml-medium", "ggml-large-v1", "ggml-large"}
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modelNames = []string{"ggml-tiny.en", "ggml-tiny", "ggml-base.en", "ggml-base", "ggml-small.en", "ggml-small", "ggml-medium.en", "ggml-medium", "ggml-large-v1", "ggml-large-v2", "ggml-large"}
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)
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var (
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@ -83,7 +83,6 @@ const (
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SampleRate = C.WHISPER_SAMPLE_RATE // Expected sample rate, samples per second
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SampleBits = uint16(unsafe.Sizeof(C.float(0))) * 8 // Sample size in bits
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NumFFT = C.WHISPER_N_FFT
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NumMEL = C.WHISPER_N_MEL
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HopLength = C.WHISPER_HOP_LENGTH
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ChunkSize = C.WHISPER_CHUNK_SIZE
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)
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@ -23,7 +23,9 @@ void bench_main(size_t index) {
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fprintf(stderr, "%s: running benchmark with %d threads - please wait...\n", __func__, n_threads);
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if (int ret = whisper_set_mel(ctx, nullptr, 0, WHISPER_N_MEL)) {
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const int n_mels = whisper_model_n_mels(ctx);
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if (int ret = whisper_set_mel(ctx, nullptr, 0, n_mels)) {
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fprintf(stderr, "error: failed to set mel: %d\n", ret);
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return;
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}
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@ -73,7 +73,9 @@ int whisper_bench_full(const whisper_params & params) {
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return 2;
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}
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if (int ret = whisper_set_mel(ctx, nullptr, 0, WHISPER_N_MEL)) {
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const int n_mels = whisper_model_n_mels(ctx);
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if (int ret = whisper_set_mel(ctx, nullptr, 0, n_mels)) {
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fprintf(stderr, "error: failed to set mel: %d\n", ret);
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return 3;
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}
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@ -48,7 +48,7 @@ if [ -n "$3" ]; then
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fi
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# Whisper models
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
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# list available models
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function list_models {
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@ -21,7 +21,7 @@ help()
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echo "Usage: ./twitch.sh -s [step] -m [model] -t [threads] [url]"
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echo "options:"
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echo "-s Step in seconds (default is $step)."
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echo "-m Choose model, options are: 'tiny.en' 'tiny' 'base.en' 'base' 'small.en' 'small' 'medium.en' 'medium' 'large-v1' 'large' (default is '$model')."
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echo "-m Choose model, options are: 'tiny.en' 'tiny' 'base.en' 'base' 'small.en' 'small' 'medium.en' 'medium' 'large-v1' 'large-v2' 'large' (default is '$model')."
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echo "-t Number of threads to use."
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echo "-h Print this help page."
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echo
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@ -1,6 +1,6 @@
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#!/bin/bash
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
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for model in "${models[@]}"; do
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python3 models/convert-pt-to-ggml.py ~/.cache/whisper/$model.pt ../whisper models/
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@ -50,7 +50,8 @@ https://huggingface.co/ggerganov/whisper.cpp/tree/main
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| medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
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| medium.en | 1.5 GB | ~2.6 GB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
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| large-v1 | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
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| large | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
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| large-v2 | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
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| large | 2.9 GB | ~4.7 GB | `ad82bf6a9043ceed055076d0fd39f5f186ff8062` |
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## Model files for testing purposes
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@ -78,14 +78,14 @@ def convert_hf_whisper(hf_model_name_or_path: str, whisper_state_path: str):
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# Ported from models/convert-whisper-to-coreml.py
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model-name", type=str, help="name of model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
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parser.add_argument("--model-name", type=str, help="name of model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
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parser.add_argument("--model-path", type=str, help="path to the model (e.g. if published on HuggingFace: Oblivion208/whisper-tiny-cantonese)", required=True)
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parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
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parser.add_argument("--quantize", type=bool, help="quantize weights to F16", default=False)
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parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
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args = parser.parse_args()
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if args.model_name not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
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if args.model_name not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1", "large-v2"]:
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raise ValueError("Invalid model name")
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pt_target_path = f"models/hf-{args.model_name}.pt"
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@ -228,7 +228,7 @@ with np.load(dir_whisper / "whisper" / "assets" / "mel_filters.npz") as f:
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# for backwards compatibility, also check for older hf_transformers format tokenizer files
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# old format: dir_whisper/whisper/assets/[multilingual/gpt2]/vocab.json
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# new format: dir_whisper/whisper/assets/[multilingual/gpt2].tiktoken
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multilingual = hparams["n_vocab"] == 51865
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multilingual = hparams["n_vocab"] >= 51865
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tokenizer = dir_whisper / "whisper" / "assets" / (multilingual and "multilingual.tiktoken" or "gpt2.tiktoken")
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tokenizer_type = "tiktoken"
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if not tokenizer.is_file():
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@ -194,7 +194,7 @@ class TextDecoderANE(TextDecoder):
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x = x.permute(0,2,3,1).squeeze(0)
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# ANE can only load tensors with dim size of at most 16,384 - whisper uses 51,864 (en) or 51,865 (multi-lang) tokens so we need to compute in chunks
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if self.token_embedding.weight.shape[0] == 51865:
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if self.token_embedding.weight.shape[0] >= 51865:
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# split in 11 chunks - 4715 each
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splits = self.token_embedding.weight.split(self.token_embedding.weight.shape[0]//11, dim=0)
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logits = torch.cat([torch.einsum('bid,jd->bij', x, split) for split in splits]).view(*x.shape[:2], -1)
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@ -296,13 +296,13 @@ def convert_decoder(hparams, model, quantize=False):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
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parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
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parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
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parser.add_argument("--quantize", type=bool, help="quantize weights to F16", default=False)
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parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
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args = parser.parse_args()
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if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
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if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1", "large-v2"]:
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raise ValueError("Invalid model name")
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whisper = load_model(args.model).cpu()
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@ -38,10 +38,10 @@ def convert_encoder(hparams, encoder, mname):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1)", required=True)
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parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large, large-v1, large-v2)", required=True)
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args = parser.parse_args()
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if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1"]:
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if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large", "large-v1", "large-v2"]:
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raise ValueError("Invalid model name")
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whisper = load_model(args.model).cpu()
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@ -19,7 +19,7 @@ function get_script_path() {
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models_path="$(get_script_path)"
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# Whisper models
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
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# list available models
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function list_models {
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@ -8,7 +8,7 @@ popd
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set argc=0
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for %%x in (%*) do set /A argc+=1
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set models=tiny.en tiny base.en base small.en small medium.en medium large-v1 large
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set models=tiny.en tiny base.en base small.en small medium.en medium large-v1 large-v2 large
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if %argc% neq 1 (
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echo.
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@ -57,8 +57,8 @@ goto :eof
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:list_models
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echo.
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echo Available models:
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(for %%a in (%models%) do (
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echo %%a
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(for %%a in (%models%) do (
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echo %%a
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))
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echo.
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exit /b
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@ -41,6 +41,7 @@ models=(
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"medium-q5_0"
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"medium.en-q5_0"
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"large-v1"
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"large-v2"
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"large"
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"large-q5_0"
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)
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@ -19,7 +19,7 @@
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cd `dirname $0`
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# Whisper models
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
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models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large-v2" "large" )
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# list available models
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function list_models {
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54
whisper.cpp
54
whisper.cpp
@ -193,6 +193,15 @@ enum e_model {
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MODEL_LARGE,
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};
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static const std::map<e_model, std::string> g_model_name = {
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{ MODEL_UNKNOWN, "unknown" },
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{ MODEL_TINY, "tiny" },
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{ MODEL_BASE, "base" },
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{ MODEL_SMALL, "small" },
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{ MODEL_MEDIUM, "medium" },
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{ MODEL_LARGE, "large" },
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};
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static const std::map<std::string, std::pair<int, std::string>> g_lang = {
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{ "en", { 0, "english", } },
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{ "zh", { 1, "chinese", } },
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@ -293,6 +302,7 @@ static const std::map<std::string, std::pair<int, std::string>> g_lang = {
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{ "ba", { 96, "bashkir", } },
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{ "jw", { 97, "javanese", } },
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{ "su", { 98, "sundanese", } },
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{ "yue", { 99, "cantonese", } },
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};
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static const size_t MB = 1ull*1024*1024;
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@ -402,7 +412,11 @@ struct whisper_vocab {
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id token_beg = 50363; // begin timestamps
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bool is_multilingual() const {
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return n_vocab == 51865;
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return n_vocab >= 51865;
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}
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int num_languages() const {
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return n_vocab - 51765 - (is_multilingual() ? 1 : 0);
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}
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};
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@ -922,6 +936,8 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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assert(hparams.n_text_state == hparams.n_audio_state);
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std::string mver = "";
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if (hparams.n_audio_layer == 4) {
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model.type = e_model::MODEL_TINY;
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}
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@ -940,6 +956,10 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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if (hparams.n_audio_layer == 32) {
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model.type = e_model::MODEL_LARGE;
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if (hparams.n_vocab == 51866) {
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mver = " v3";
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}
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}
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const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
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@ -968,7 +988,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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log("%s: n_mels = %d\n", __func__, hparams.n_mels);
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log("%s: ftype = %d\n", __func__, model.hparams.ftype);
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log("%s: qntvr = %d\n", __func__, qntvr);
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log("%s: type = %d\n", __func__, model.type);
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log("%s: type = %d (%s%s)\n", __func__, model.type, g_model_name.at(model.type).c_str(), mver.c_str());
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// print memory requirements
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{
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@ -1039,13 +1059,17 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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if (vocab.is_multilingual()) {
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vocab.token_eot++;
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vocab.token_sot++;
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vocab.token_translate++;
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vocab.token_transcribe++;
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vocab.token_solm++;
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vocab.token_prev++;
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vocab.token_nosp++;
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vocab.token_not++;
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vocab.token_beg++;
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// account for variable number of language tokens
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const int dt = vocab.num_languages() - 98;
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vocab.token_translate += dt;
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vocab.token_transcribe += dt;
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vocab.token_solm += dt;
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vocab.token_prev += dt;
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vocab.token_nosp += dt;
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vocab.token_not += dt;
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vocab.token_beg += dt;
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}
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if (n_vocab < model.hparams.n_vocab) {
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@ -1074,6 +1098,8 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
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vocab.id_to_token[i] = word;
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}
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}
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log("%s: n_langs = %d\n", __func__, vocab.num_languages());
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}
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size_t ctx_size = 0;
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@ -3281,7 +3307,7 @@ void whisper_free_params(struct whisper_full_params * params) {
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}
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int whisper_pcm_to_mel_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
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if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, false, state->mel)) {
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if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
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log("%s: failed to compute mel spectrogram\n", __func__);
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return -1;
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}
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@ -3295,7 +3321,7 @@ int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int
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|
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// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2 (PV without phase lock is not good)
|
||||
int whisper_pcm_to_mel_phase_vocoder_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
|
||||
if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, 2 * WHISPER_N_FFT, 2 * WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, false, state->mel)) {
|
||||
if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, 2 * WHISPER_N_FFT, 2 * WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
|
||||
log("%s: failed to compute mel spectrogram\n", __func__);
|
||||
return -1;
|
||||
}
|
||||
@ -3318,13 +3344,13 @@ int whisper_pcm_to_mel_phase_vocoder(struct whisper_context * ctx, const float *
|
||||
// TODO
|
||||
|
||||
int whisper_set_mel_with_state(
|
||||
struct whisper_context * /*ctx*/,
|
||||
struct whisper_context * ctx,
|
||||
struct whisper_state * state,
|
||||
const float * data,
|
||||
int n_len,
|
||||
int n_mel) {
|
||||
if (n_mel != WHISPER_N_MEL) {
|
||||
log("%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, WHISPER_N_MEL);
|
||||
if (n_mel != ctx->model.filters.n_mel) {
|
||||
log("%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, ctx->model.filters.n_mel);
|
||||
return -1;
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user