Add C++ runtime and Python APIs for Moonshine models

.github/scripts/test-offline-moonshine.sh

@@ -0,0 +1,50 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+export GIT_CLONE_PROTECTION_ACTIVE=false
+
+echo "EXE is $EXE"
+echo "PATH: $PATH"
+
+which $EXE
+
+names=(
+tiny
+base
+)
+
+for name in ${names[@]}; do
+  log "------------------------------------------------------------"
+  log "Run $name"
+  log "------------------------------------------------------------"
+
+  repo_url=https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-$name.tar.bz2
+  repo_url=https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-moonshine-$name-en-int8.tar.bz2
+  curl -SL -O $repo_url
+  tar xvf sherpa-onnx-moonshine-$name-en-int8.tar.bz2
+  rm sherpa-onnx-moonshine-$name-en-int8.tar.bz2
+  repo=sherpa-onnx-moonshine-$name-en-int8
+  log "Start testing ${repo_url}"
+
+  log "test int8 onnx"
+
+  time $EXE \
+    --moonshine-preprocessor=$repo/preprocess.onnx \
+    --moonshine-encoder=$repo/encode.int8.onnx \
+    --moonshine-uncached-decoder=$repo/uncached_decode.int8.onnx \
+    --moonshine-cached-decoder=$repo/cached_decode.int8.onnx \
+    --tokens=$repo/tokens.txt \
+    --num-threads=2 \
+    $repo/test_wavs/0.wav \
+    $repo/test_wavs/1.wav \
+    $repo/test_wavs/8k.wav
+
+  rm -rf $repo
+done

.github/scripts/test-python.sh

@@ -8,6 +8,16 @@ log() {
   echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
 }
 
+log "test offline Moonshine"
+
+curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
+tar xvf sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
+rm sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
+
+python3 ./python-api-examples/offline-moonshine-decode-files.py
+
+rm -rf sherpa-onnx-moonshine-tiny-en-int8
+
 log "test offline speaker diarization"
 
 curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2

.github/workflows/linux.yaml

@@ -149,6 +149,19 @@ jobs:
           name: release-${{ matrix.build_type }}-with-shared-lib-${{ matrix.shared_lib }}-with-tts-${{ matrix.with_tts }}
           path: install/*
 
+      - name: Test offline Moonshine
+        if: matrix.build_type != 'Debug'
+        shell: bash
+        run: |
+          du -h -d1 .
+          export PATH=$PWD/build/bin:$PATH
+          export EXE=sherpa-onnx-offline
+
+          readelf -d build/bin/sherpa-onnx-offline
+
+          .github/scripts/test-offline-moonshine.sh
+          du -h -d1 .
+
       - name: Test offline CTC
         shell: bash
         run: |

.github/workflows/macos.yaml

@@ -121,6 +121,15 @@ jobs:
           otool -L build/bin/sherpa-onnx
           otool -l build/bin/sherpa-onnx
 
+      - name: Test offline Moonshine
+        if: matrix.build_type != 'Debug'
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin:$PATH
+          export EXE=sherpa-onnx-offline
+
+          .github/scripts/test-offline-moonshine.sh
+
       - name: Test C++ API
         shell: bash
         run: |
@@ -243,8 +252,6 @@ jobs:
 
           .github/scripts/test-offline-whisper.sh
 
-
-
       - name: Test online transducer
         shell: bash
         run: |

.github/workflows/windows-x64.yaml

@@ -93,6 +93,14 @@ jobs:
           name: release-windows-x64-${{ matrix.shared_lib }}-${{ matrix.with_tts }}
           path: build/install/*
 
+      - name: Test offline Moonshine for windows x64
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin/Release:$PATH
+          export EXE=sherpa-onnx-offline.exe
+
+          .github/scripts/test-offline-moonshine.sh
+
       - name: Test C++ API
         shell: bash
         run: |

.github/workflows/windows-x86.yaml

@@ -93,6 +93,14 @@ jobs:
           name: release-windows-x86-${{ matrix.shared_lib }}-${{ matrix.with_tts }}
           path: build/install/*
 
+      - name: Test offline Moonshine for windows x86
+        shell: bash
+        run: |
+          export PATH=$PWD/build/bin/Release:$PATH
+          export EXE=sherpa-onnx-offline.exe
+
+          .github/scripts/test-offline-moonshine.sh
+
       - name: Test C++ API
         shell: bash
         run: |

python-api-examples/generate-subtitles.py

@@ -47,7 +47,19 @@
       --feature-dim=80 \
       /path/to/test.mp4
 
-(3) For Whisper models
+(3) For Moonshine models
+
+./python-api-examples/generate-subtitles.py  \
+  --silero-vad-model=/path/to/silero_vad.onnx \
+  --moonshine-preprocessor=./sherpa-onnx-moonshine-tiny-en-int8/preprocess.onnx \
+  --moonshine-encoder=./sherpa-onnx-moonshine-tiny-en-int8/encode.int8.onnx \
+  --moonshine-uncached-decoder=./sherpa-onnx-moonshine-tiny-en-int8/uncached_decode.int8.onnx \
+  --moonshine-cached-decoder=./sherpa-onnx-moonshine-tiny-en-int8/cached_decode.int8.onnx \
+  --tokens=./sherpa-onnx-moonshine-tiny-en-int8/tokens.txt \
+  --num-threads=2 \
+  /path/to/test.mp4
+
+(4) For Whisper models
 
 ./python-api-examples/generate-subtitles.py  \
   --silero-vad-model=/path/to/silero_vad.onnx \
@@ -58,7 +70,7 @@
   --num-threads=2 \
   /path/to/test.mp4
 
-(4) For SenseVoice CTC models
+(5) For SenseVoice CTC models
 
 ./python-api-examples/generate-subtitles.py  \
   --silero-vad-model=/path/to/silero_vad.onnx \
@@ -68,7 +80,7 @@
   /path/to/test.mp4
 
 
-(5) For WeNet CTC models
+(6) For WeNet CTC models
 
 ./python-api-examples/generate-subtitles.py  \
   --silero-vad-model=/path/to/silero_vad.onnx \
@@ -83,6 +95,7 @@
 used in this file.
 """
 import argparse
+import datetime as dt
 import shutil
 import subprocess
 import sys
@@ -157,7 +170,7 @@ def get_args():
     parser.add_argument(
         "--num-threads",
         type=int,
-        default=1,
+        default=2,
         help="Number of threads for neural network computation",
     )
 
@@ -208,6 +221,34 @@ def get_args():
         """,
     )
 
+    parser.add_argument(
+        "--moonshine-preprocessor",
+        default="",
+        type=str,
+        help="Path to moonshine preprocessor model",
+    )
+
+    parser.add_argument(
+        "--moonshine-encoder",
+        default="",
+        type=str,
+        help="Path to moonshine encoder model",
+    )
+
+    parser.add_argument(
+        "--moonshine-uncached-decoder",
+        default="",
+        type=str,
+        help="Path to moonshine uncached decoder model",
+    )
+
+    parser.add_argument(
+        "--moonshine-cached-decoder",
+        default="",
+        type=str,
+        help="Path to moonshine cached decoder model",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -263,6 +304,12 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
         assert len(args.wenet_ctc) == 0, args.wenet_ctc
         assert len(args.whisper_encoder) == 0, args.whisper_encoder
         assert len(args.whisper_decoder) == 0, args.whisper_decoder
+        assert len(args.moonshine_preprocessor) == 0, args.moonshine_preprocessor
+        assert len(args.moonshine_encoder) == 0, args.moonshine_encoder
+        assert (
+            len(args.moonshine_uncached_decoder) == 0
+        ), args.moonshine_uncached_decoder
+        assert len(args.moonshine_cached_decoder) == 0, args.moonshine_cached_decoder
 
         assert_file_exists(args.encoder)
         assert_file_exists(args.decoder)
@@ -284,6 +331,12 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
         assert len(args.wenet_ctc) == 0, args.wenet_ctc
         assert len(args.whisper_encoder) == 0, args.whisper_encoder
         assert len(args.whisper_decoder) == 0, args.whisper_decoder
+        assert len(args.moonshine_preprocessor) == 0, args.moonshine_preprocessor
+        assert len(args.moonshine_encoder) == 0, args.moonshine_encoder
+        assert (
+            len(args.moonshine_uncached_decoder) == 0
+        ), args.moonshine_uncached_decoder
+        assert len(args.moonshine_cached_decoder) == 0, args.moonshine_cached_decoder
 
         assert_file_exists(args.paraformer)
 
@@ -300,6 +353,12 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
         assert len(args.wenet_ctc) == 0, args.wenet_ctc
         assert len(args.whisper_encoder) == 0, args.whisper_encoder
         assert len(args.whisper_decoder) == 0, args.whisper_decoder
+        assert len(args.moonshine_preprocessor) == 0, args.moonshine_preprocessor
+        assert len(args.moonshine_encoder) == 0, args.moonshine_encoder
+        assert (
+            len(args.moonshine_uncached_decoder) == 0
+        ), args.moonshine_uncached_decoder
+        assert len(args.moonshine_cached_decoder) == 0, args.moonshine_cached_decoder
 
         assert_file_exists(args.sense_voice)
         recognizer = sherpa_onnx.OfflineRecognizer.from_sense_voice(
@@ -312,6 +371,12 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
     elif args.wenet_ctc:
         assert len(args.whisper_encoder) == 0, args.whisper_encoder
         assert len(args.whisper_decoder) == 0, args.whisper_decoder
+        assert len(args.moonshine_preprocessor) == 0, args.moonshine_preprocessor
+        assert len(args.moonshine_encoder) == 0, args.moonshine_encoder
+        assert (
+            len(args.moonshine_uncached_decoder) == 0
+        ), args.moonshine_uncached_decoder
+        assert len(args.moonshine_cached_decoder) == 0, args.moonshine_cached_decoder
 
         assert_file_exists(args.wenet_ctc)
 
@@ -327,6 +392,12 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
     elif args.whisper_encoder:
         assert_file_exists(args.whisper_encoder)
         assert_file_exists(args.whisper_decoder)
+        assert len(args.moonshine_preprocessor) == 0, args.moonshine_preprocessor
+        assert len(args.moonshine_encoder) == 0, args.moonshine_encoder
+        assert (
+            len(args.moonshine_uncached_decoder) == 0
+        ), args.moonshine_uncached_decoder
+        assert len(args.moonshine_cached_decoder) == 0, args.moonshine_cached_decoder
 
         recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
             encoder=args.whisper_encoder,
@@ -339,6 +410,22 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
             task=args.whisper_task,
             tail_paddings=args.whisper_tail_paddings,
         )
+    elif args.moonshine_preprocessor:
+        assert_file_exists(args.moonshine_preprocessor)
+        assert_file_exists(args.moonshine_encoder)
+        assert_file_exists(args.moonshine_uncached_decoder)
+        assert_file_exists(args.moonshine_cached_decoder)
+
+        recognizer = sherpa_onnx.OfflineRecognizer.from_moonshine(
+            preprocessor=args.moonshine_preprocessor,
+            encoder=args.moonshine_encoder,
+            uncached_decoder=args.moonshine_uncached_decoder,
+            cached_decoder=args.moonshine_cached_decoder,
+            tokens=args.tokens,
+            num_threads=args.num_threads,
+            decoding_method=args.decoding_method,
+            debug=args.debug,
+        )
     else:
         raise ValueError("Please specify at least one model")
 
@@ -424,28 +511,32 @@ def main():
     segment_list = []
 
     print("Started!")
+    start_t = dt.datetime.now()
+    num_processed_samples = 0
 
-    is_silence = False
+    is_eof = False
     # TODO(fangjun): Support multithreads
     while True:
         # *2 because int16_t has two bytes
         data = process.stdout.read(frames_per_read * 2)
         if not data:
-            if is_silence:
+            if is_eof:
                 break
-            is_silence = True
-            # The converted audio file does not have a mute data of 1 second or more at the end, which will result in the loss of the last segment data
+            is_eof = True
+            # pad 1 second at the end of the file for the VAD
             data = np.zeros(1 * args.sample_rate, dtype=np.int16)
 
         samples = np.frombuffer(data, dtype=np.int16)
         samples = samples.astype(np.float32) / 32768
 
+        num_processed_samples += samples.shape[0]
+
         buffer = np.concatenate([buffer, samples])
         while len(buffer) > window_size:
             vad.accept_waveform(buffer[:window_size])
             buffer = buffer[window_size:]
 
-        if is_silence:
+        if is_eof:
             vad.flush()
 
         streams = []
@@ -471,6 +562,11 @@ def main():
             seg.text = stream.result.text
             segment_list.append(seg)
 
+    end_t = dt.datetime.now()
+    elapsed_seconds = (end_t - start_t).total_seconds()
+    duration = num_processed_samples / 16000
+    rtf = elapsed_seconds / duration
+
     srt_filename = Path(args.sound_file).with_suffix(".srt")
     with open(srt_filename, "w", encoding="utf-8") as f:
         for i, seg in enumerate(segment_list):
@@ -479,6 +575,9 @@ def main():
             print("", file=f)
 
     print(f"Saved to {srt_filename}")
+    print(f"Audio duration:\t{duration:.3f} s")
+    print(f"Elapsed:\t{elapsed_seconds:.3f} s")
+    print(f"RTF = {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
     print("Done!")