Support whisper large/large-v1/large-v2/large-v3 and distil-large-v2 (#1114)

@@ -15,9 +15,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
-        # model: ["distil-medium.en", "distil-small.en",  "tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large", "large-v1", "large-v2", "distil-large-v2"]
-        model: ["distil-medium.en", "distil-small.en",  "tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "medium-aishell"]
+        os: [macos-latest]
+        model: ["distil-medium.en", "distil-small.en",  "tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "medium-aishell", "large", "large-v1", "large-v2", "distil-large-v2"]
+        # model: ["large", "large-v1", "large-v2", "large-v3", "distil-large-v2"]
         python-version: ["3.8"]
 
     steps:
@@ -32,7 +32,7 @@ jobs:
         shell: bash
         run: |
           python3 -m pip install torch==1.13.0 torchaudio==0.13.0 -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          python3 -m pip install openai-whisper==20230314 onnxruntime onnx
+          python3 -m pip install openai-whisper==20231117 onnxruntime onnx soundfile librosa
 
       - name: export ${{ matrix.model }}
         shell: bash
@@ -62,7 +62,6 @@ jobs:
             rm -fv medium-aishell-decoder.onnx
           fi
 
-
           ls -lh
 
           ls -lh ~/.cache/whisper || true
@@ -74,7 +73,8 @@ jobs:
           src=sherpa-onnx-whisper-${{ matrix.model }}
 
           cd ..
-          mv whisper $src
+          mkdir $src
+          mv -v whisper/$model* $src/
 
           echo "------------------------------"
 
@@ -97,19 +97,16 @@ jobs:
           ls -lh $src
           echo "--------------------"
 
-          if [[ $model == large || $model == large-v1 || $model == large-v2 || $model == distil-large-v2 ]]; then
-            #tar cvjf - $src | split --bytes=1024MB - $src.tar.bz2.
-            tar cvjf $src.tar.bz2 $src
-            split -b 1G $src.tar.bz2 $src.tar.bz2.
-            rm $src.tar.bz2
-            # cat $src.tar.gz.* | tar xjf -
+          if [[ $model == large || $model == large-v1 || $model == large-v2 || $model == distil-large-v2  ]]; then
+            echo "Don't release model to github for large models. $model"
           else
             tar cvjf $src.tar.bz2 $src
           fi
-          ls -lh
 
+          ls -lh
 
       - name: Release
+        if: matrix.model != 'large' && matrix.model != 'large-v1' && matrix.model != 'large-v2' && matrix.model != 'large-v3' && matrix.model != 'distil-large-v2'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
@@ -119,19 +116,6 @@ jobs:
           repo_token: ${{ secrets.UPLOAD_GH_SHERPA_ONNX_TOKEN }}
           tag: asr-models
 
-      - name: Test ${{ matrix.model }}
-        shell: bash
-        run: |
-          python3 -m pip install kaldi-native-fbank
-          git checkout .
-          model=${{ matrix.model }}
-          src=sherpa-onnx-whisper-$model
-          python3 scripts/whisper/test.py \
-            --encoder $src/$model-encoder.int8.onnx \
-            --decoder $src/$model-decoder.int8.onnx \
-            --tokens $src/$model-tokens.txt \
-            $src/test_wavs/0.wav
-
       - name: Publish ${{ matrix.model }} to huggingface
         shell: bash
         env:
@@ -144,27 +128,36 @@ jobs:
 
           export GIT_CLONE_PROTECTION_ACTIVE=false
 
-          GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/csukuangfj/sherpa-onnx-whisper-${{ matrix.model }} huggingface
+          export GIT_LFS_SKIP_SMUDGE=1
+
+          git clone https://huggingface.co/csukuangfj/sherpa-onnx-whisper-${{ matrix.model }} huggingface
 
           if [[ $model != medium-aishell ]]; then
             rm -rf huggingface/*
           fi
 
-          if [[ $model == large || $model == large-v1 || $model == large-v2 || $model == distil-large-v2 ]]; then
-            mv $src.tar* ./huggingface
-          else
-            cp -v $src/*.onnx ./huggingface
-            cp -v $src/*tokens* ./huggingface
-            cp -av $src/test_wavs ./huggingface
-          fi
+          cp -av $src/* ./huggingface/
 
           cd huggingface
 
           git status
           ls -lh
-          git lfs track "*gz*"
           git lfs track "*onnx*"
+          git lfs track "*weights*"
 
           git add .
           git commit -m "upload ${{ matrix.model }}"
           git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/sherpa-onnx-whisper-${{ matrix.model }} main
+
+      - name: Test ${{ matrix.model }}
+        shell: bash
+        run: |
+          python3 -m pip install kaldi-native-fbank
+          git checkout .
+          model=${{ matrix.model }}
+          src=sherpa-onnx-whisper-$model
+          time python3 scripts/whisper/test.py \
+            --encoder $src/$model-encoder.onnx \
+            --decoder $src/$model-decoder.onnx \
+            --tokens $src/$model-tokens.txt \
+            $src/test_wavs/0.wav

-## 1.10.14 (to-be-released)
+## 1.10.14
 
+* Support whisper large v3
 * Update onnxruntime from v1.18.0 to v1.18.1
 * Fix invalid utf8 sequence from Whisper for Dart API.
 

@@ -11,7 +11,7 @@ project(sherpa-onnx)
 # ./nodejs-addon-examples
 # ./dart-api-examples/
 # ./CHANGELOG.md
-set(SHERPA_ONNX_VERSION "1.10.13")
+set(SHERPA_ONNX_VERSION "1.10.14")
 
 # Disable warning about
 #

 function(download_kaldi_native_fbank)
   include(FetchContent)
 
-  set(kaldi_native_fbank_URL   "https://github.com/csukuangfj/kaldi-native-fbank/archive/refs/tags/v1.19.3.tar.gz")
-  set(kaldi_native_fbank_URL2  "https://hub.nuaa.cf/csukuangfj/kaldi-native-fbank/archive/refs/tags/v1.19.3.tar.gz")
-  set(kaldi_native_fbank_HASH "SHA256=335fe1daf1b9bfb2a7b6bf03b64c4c4686c39077c57fb8058c02611981676638")
+  set(kaldi_native_fbank_URL   "https://github.com/csukuangfj/kaldi-native-fbank/archive/refs/tags/v1.20.0.tar.gz")
+  set(kaldi_native_fbank_URL2  "https://hub.nuaa.cf/csukuangfj/kaldi-native-fbank/archive/refs/tags/v1.20.0.tar.gz")
+  set(kaldi_native_fbank_HASH "SHA256=c6195b3cf374eef824644061d3c04f6b2a9267ae554169cbaa9865c89c1fe4f9")
 
   set(KALDI_NATIVE_FBANK_BUILD_TESTS OFF CACHE BOOL "" FORCE)
   set(KALDI_NATIVE_FBANK_BUILD_PYTHON OFF CACHE BOOL "" FORCE)
@@ -12,11 +12,11 @@ function(download_kaldi_native_fbank)
   # If you don't have access to the Internet,
   # please pre-download kaldi-native-fbank
   set(possible_file_locations
-    $ENV{HOME}/Downloads/kaldi-native-fbank-1.19.3.tar.gz
-    ${CMAKE_SOURCE_DIR}/kaldi-native-fbank-1.19.3.tar.gz
-    ${CMAKE_BINARY_DIR}/kaldi-native-fbank-1.19.3.tar.gz
-    /tmp/kaldi-native-fbank-1.19.3.tar.gz
-    /star-fj/fangjun/download/github/kaldi-native-fbank-1.19.3.tar.gz
+    $ENV{HOME}/Downloads/kaldi-native-fbank-1.20.0.tar.gz
+    ${CMAKE_SOURCE_DIR}/kaldi-native-fbank-1.20.0.tar.gz
+    ${CMAKE_BINARY_DIR}/kaldi-native-fbank-1.20.0.tar.gz
+    /tmp/kaldi-native-fbank-1.20.0.tar.gz
+    /star-fj/fangjun/download/github/kaldi-native-fbank-1.20.0.tar.gz
   )
 
   foreach(f IN LISTS possible_file_locations)

@@ -10,7 +10,7 @@ environment:
 
 # Add regular dependencies here.
 dependencies:
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   path: ^1.9.0
   args: ^2.5.0
 

@@ -11,7 +11,7 @@ environment:
 
 # Add regular dependencies here.
 dependencies:
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   path: ^1.9.0
   args: ^2.5.0
 

@@ -8,7 +8,7 @@ environment:
 
 # Add regular dependencies here.
 dependencies:
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   path: ^1.9.0
   args: ^2.5.0
 

@@ -9,7 +9,7 @@ environment:
   sdk: ^3.4.0
 
 dependencies:
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   path: ^1.9.0
   args: ^2.5.0
 

@@ -5,7 +5,7 @@ description: >
 
 publish_to: 'none'
 
-version: 1.10.13
+version: 1.10.14
 
 topics:
   - speech-recognition
@@ -30,7 +30,7 @@ dependencies:
   record: ^5.1.0
   url_launcher: ^6.2.6
 
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   # sherpa_onnx:
     # path: ../../flutter/sherpa_onnx
 

@@ -17,7 +17,7 @@ dependencies:
   cupertino_icons: ^1.0.6
   path_provider: ^2.1.3
   path: ^1.9.0
-  sherpa_onnx: ^1.10.13
+  sherpa_onnx: ^1.10.14
   url_launcher: ^6.2.6
   audioplayers: ^5.0.0
 

@@ -17,7 +17,7 @@ topics:
   - voice-activity-detection
 
 # remember to change the version in ../sherpa_onnx_macos/macos/sherpa_onnx_macos.podspec
-version: 1.10.13
+version: 1.10.14
 
 homepage: https://github.com/k2-fsa/sherpa-onnx
 
@@ -30,19 +30,19 @@ dependencies:
   flutter:
     sdk: flutter
 
-  sherpa_onnx_android: ^1.10.13
+  sherpa_onnx_android: ^1.10.14
     # path: ../sherpa_onnx_android
 
-  sherpa_onnx_macos: ^1.10.13
+  sherpa_onnx_macos: ^1.10.14
     # path: ../sherpa_onnx_macos
 
-  sherpa_onnx_linux: ^1.10.13
+  sherpa_onnx_linux: ^1.10.14
     # path: ../sherpa_onnx_linux
     #
-  sherpa_onnx_windows: ^1.10.13
+  sherpa_onnx_windows: ^1.10.14
     # path: ../sherpa_onnx_windows
 
-  sherpa_onnx_ios: ^1.10.13
+  sherpa_onnx_ios: ^1.10.14
   # sherpa_onnx_ios:
     # path: ../sherpa_onnx_ios
 

@@ -7,7 +7,7 @@
 # https://groups.google.com/g/dart-ffi/c/nUATMBy7r0c
 Pod::Spec.new do |s|
   s.name             = 'sherpa_onnx_ios'
-  s.version          = '1.10.13'
+  s.version          = '1.10.14'
   s.summary          = 'A new Flutter FFI plugin project.'
   s.description      = <<-DESC
 A new Flutter FFI plugin project.

@@ -4,7 +4,7 @@
 #
 Pod::Spec.new do |s|
   s.name             = 'sherpa_onnx_macos'
-  s.version          = '1.10.13'
+  s.version          = '1.10.14'
   s.summary          = 'sherpa-onnx Flutter FFI plugin project.'
   s.description      = <<-DESC
 sherpa-onnx Flutter FFI plugin project.

 {
   "dependencies": {
-    "sherpa-onnx-node": "^1.10.13"
+    "sherpa-onnx-node": "^1.10.14"
   }
 }

@@ -17,7 +17,7 @@ topics:
   - voice-activity-detection
 
 # remember to change the version in ../sherpa_onnx_macos/macos/sherpa_onnx.podspec
-version: 1.10.13
+version: 1.10.14
 
 homepage: https://github.com/k2-fsa/sherpa-onnx
 

@@ -2,3 +2,9 @@
 *.config
 *.ort
 *-tokens.txt
+*.bias
+*.weights
+*.weight
+*.*embedding
+_Const*
+onnx__*

@@ -32,6 +32,9 @@ from whisper.model import (
     TextDecoder,
 )
 
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
 
 def get_args():
     parser = argparse.ArgumentParser()
@@ -43,8 +46,9 @@ def get_args():
         choices=[
             "tiny", "tiny.en", "base", "base.en",
             "small", "small.en", "medium", "medium.en",
-            "large", "large-v1", "large-v2",
+            "large", "large-v1", "large-v2", "large-v3",
             "distil-medium.en", "distil-small.en", "distil-large-v2",
+            # "distil-large-v3", # distil-large-v3 is not supported!
             # for fine-tuned models from icefall
             "medium-aishell",
             ],
@@ -63,12 +67,26 @@ def add_meta_data(filename: str, meta_data: Dict[str, Any]):
         Key-value pairs.
     """
     model = onnx.load(filename)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
     for key, value in meta_data.items():
         meta = model.metadata_props.add()
         meta.key = key
         meta.value = str(value)
 
-    onnx.save(model, filename)
+    if "large" in filename:
+        external_filename = filename.split(".onnx")[0]
+        onnx.save(
+            model,
+            filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=external_filename + ".weights",
+        )
+    else:
+        onnx.save(model, filename)
 
 
 def modified_audio_encoder_forward(self: AudioEncoder, x: torch.Tensor):
@@ -376,7 +394,9 @@ def main():
 
     # write tokens
 
-    tokenizer = whisper.tokenizer.get_tokenizer(model.is_multilingual)
+    tokenizer = whisper.tokenizer.get_tokenizer(
+        model.is_multilingual, num_languages=model.num_languages
+    )
 
     model.eval()
     print(model.dims)
@@ -384,10 +404,15 @@ def main():
     audio = whisper.pad_or_trim(audio)
     assert audio.shape == (16000 * 30,), audio.shape
 
-    # make log-Mel spectrogram and move to the same device as the model
-    mel = whisper.log_mel_spectrogram(audio).to(model.device).unsqueeze(0)
+    if args.model in ("large", "large-v3"):
+        n_mels = 128
+    else:
+        n_mels = 80
+    mel = (
+        whisper.log_mel_spectrogram(audio, n_mels=n_mels).to(model.device).unsqueeze(0)
+    )
     batch_size = 1
-    assert mel.shape == (batch_size, 80, 30 * 100)
+    assert mel.shape == (batch_size, n_mels, 30 * 100), mel.shape
 
     encoder = AudioEncoderTensorCache(model.encoder, model.decoder)
 
@@ -547,6 +572,17 @@ def main():
     )
 
     if "large" in args.model:
+        decoder_external_filename = decoder_filename.split(".onnx")[0]
+        decoder_model = onnx.load(decoder_filename)
+        onnx.save(
+            decoder_model,
+            decoder_filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=decoder_external_filename + ".weights",
+        )
+
+    if "large" in args.model:
         # it causes errors for large models, so skip it.
         return
     # Generate int8 quantization models

@@ -9,9 +9,10 @@ import base64
 from typing import Tuple
 
 import kaldi_native_fbank as knf
+import numpy as np
 import onnxruntime as ort
+import soundfile as sf
 import torch
-import torchaudio
 
 
 def get_args():
@@ -98,7 +99,6 @@ class OnnxModel:
         self.blank = int(meta["blank_id"])
 
         self.sot_sequence = list(map(int, meta["sot_sequence"].split(",")))
-
         self.sot_sequence.append(self.no_timestamps)
 
         self.all_language_tokens = list(
@@ -226,7 +226,18 @@ def load_tokens(filename):
     return tokens
 
 
-def compute_features(filename: str) -> torch.Tensor:
+def load_audio(filename: str) -> Tuple[np.ndarray, int]:
+    data, sample_rate = sf.read(
+        filename,
+        always_2d=True,
+        dtype="float32",
+    )
+    data = data[:, 0]  # use only the first channel
+    samples = np.ascontiguousarray(data)
+    return samples, sample_rate
+
+
+def compute_features(filename: str, dim: int = 80) -> torch.Tensor:
     """
     Args:
       filename:
@@ -234,16 +245,18 @@ def compute_features(filename: str) -> torch.Tensor:
     Returns:
       Return a 1-D float32 tensor of shape (1, 80, 3000) containing the features.
     """
-    wave, sample_rate = torchaudio.load(filename)
-    audio = wave[0].contiguous()  # only use the first channel
+    wave, sample_rate = load_audio(filename)
     if sample_rate != 16000:
-        audio = torchaudio.functional.resample(
-            audio, orig_freq=sample_rate, new_freq=16000
-        )
+        import librosa
+
+        wave = librosa.resample(wave, orig_sr=sample_rate, target_sr=16000)
+        sample_rate = 16000
 
     features = []
-    online_whisper_fbank = knf.OnlineWhisperFbank(knf.FrameExtractionOptions())
-    online_whisper_fbank.accept_waveform(16000, audio.numpy())
+    opts = knf.WhisperFeatureOptions()
+    opts.dim = dim
+    online_whisper_fbank = knf.OnlineWhisperFbank(opts)
+    online_whisper_fbank.accept_waveform(16000, wave)
     online_whisper_fbank.input_finished()
     for i in range(online_whisper_fbank.num_frames_ready):
         f = online_whisper_fbank.get_frame(i)
@@ -280,8 +293,9 @@ def compute_features(filename: str) -> torch.Tensor:
 def main():
     args = get_args()
 
-    mel = compute_features(args.sound_file)
     model = OnnxModel(args.encoder, args.decoder)
+    dim = 80 if "large-v3" not in args.encoder else 128
+    mel = compute_features(args.sound_file, dim=dim)
 
     n_layer_cross_k, n_layer_cross_v = model.run_encoder(mel)
 
@@ -313,6 +327,7 @@ def main():
 
     n_layer_self_k_cache, n_layer_self_v_cache = model.get_self_cache()
 
+    print(model.sot_sequence)
     tokens = torch.tensor([model.sot_sequence], dtype=torch.int64)
     offset = torch.zeros(1, dtype=torch.int64)
     logits, n_layer_self_k_cache, n_layer_self_v_cache = model.run_decoder(

@@ -88,7 +88,9 @@ class OfflineRecognizerWhisperImpl : public OfflineRecognizerImpl {
   }
 
   std::unique_ptr<OfflineStream> CreateStream() const override {
-    return std::make_unique<OfflineStream>(WhisperTag{});
+    WhisperTag tag;
+    tag.dim = model_->FeatureDim();
+    return std::make_unique<OfflineStream>(tag);
   }
 
   void DecodeStreams(OfflineStream **ss, int32_t n) const override {

@@ -97,12 +97,16 @@ class OfflineStream::Impl {
     }
   }
 
-  explicit Impl(WhisperTag /*tag*/) {
+  explicit Impl(WhisperTag tag) {
     config_.normalize_samples = true;
     opts_.frame_opts.samp_freq = 16000;
-    opts_.mel_opts.num_bins = 80;  // not used
-    whisper_fbank_ =
-        std::make_unique<knf::OnlineWhisperFbank>(opts_.frame_opts);
+    opts_.mel_opts.num_bins = tag.dim;
+
+    knf::WhisperFeatureOptions whisper_opts;
+    whisper_opts.frame_opts = opts_.frame_opts;
+    whisper_opts.dim = tag.dim;
+
+    whisper_fbank_ = std::make_unique<knf::OnlineWhisperFbank>(whisper_opts);
     config_.sampling_rate = opts_.frame_opts.samp_freq;
   }
 

@@ -35,7 +35,10 @@ struct OfflineRecognitionResult {
   std::string AsJsonString() const;
 };
 
-struct WhisperTag {};
+struct WhisperTag {
+  int32_t dim = 80;
+};
+
 struct CEDTag {};
 
 class OfflineStream {

@@ -217,6 +217,8 @@ class OfflineWhisperModel::Impl {
 
   int32_t VocabSize() const { return n_vocab_; }
 
+  int32_t FeatureDim() const { return n_mels_; }
+
   int32_t Translate() const { return translate_; }
 
   bool IsMultiLingual() const { return is_multilingual_; }
@@ -242,6 +244,7 @@ class OfflineWhisperModel::Impl {
     }
 
     Ort::AllocatorWithDefaultOptions allocator;  // used in the macro below
+    SHERPA_ONNX_READ_META_DATA(n_mels_, "n_mels");
     SHERPA_ONNX_READ_META_DATA(n_text_layer_, "n_text_layer");
     SHERPA_ONNX_READ_META_DATA(n_text_ctx_, "n_text_ctx");
     SHERPA_ONNX_READ_META_DATA(n_text_state_, "n_text_state");
@@ -316,6 +319,7 @@ class OfflineWhisperModel::Impl {
   std::unordered_map<int32_t, std::string> id2lang_;
 
   // model meta data
+  int32_t n_mels_ = 80;
   int32_t n_text_layer_ = 0;
   int32_t n_text_ctx_ = 0;
   int32_t n_text_state_ = 0;
@@ -414,6 +418,8 @@ int32_t OfflineWhisperModel::TextCtx() const { return impl_->TextCtx(); }
 
 int32_t OfflineWhisperModel::VocabSize() const { return impl_->VocabSize(); }
 
+int32_t OfflineWhisperModel::FeatureDim() const { return impl_->FeatureDim(); }
+
 int32_t OfflineWhisperModel::Translate() const { return impl_->Translate(); }
 
 bool OfflineWhisperModel::IsMultiLingual() const {

@@ -102,6 +102,7 @@ class OfflineWhisperModel {
   int32_t SOT() const;
   int32_t TextCtx() const;
   int32_t VocabSize() const;
+  int32_t FeatureDim() const;
   int32_t Translate() const;
   bool IsMultiLingual() const;