update

.gitattributes

@@ -109,3 +109,19 @@ assets/en_woman1.mp3 filter=lfs diff=lfs merge=lfs -text
 assets/zh_man1.txt filter=lfs diff=lfs merge=lfs -text
 assets/zh_woman1.txt filter=lfs diff=lfs merge=lfs -text
 assets/en_man1.mp3 filter=lfs diff=lfs merge=lfs -text
+feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l2_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p5_l0_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p5_l1_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p5_l2_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l1_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l3_together.axmodel filter=lfs diff=lfs merge=lfs -text
+axmodels/stop_predictor.onnx filter=lfs diff=lfs merge=lfs -text
+feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p5_l3_together.axmodel filter=lfs diff=lfs merge=lfs -text
+feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l0_together.axmodel filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib/cmake filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime_providers_shared.so filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime.so filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime.so.1.16.0 filter=lfs diff=lfs merge=lfs -text
+onnxruntime-aarch64-none-gnu-1.16.0/lib/pkgconfig filter=lfs diff=lfs merge=lfs -text
+main_ax650 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -36,7 +36,7 @@ For those who are interested in model conversion, you can try to export axmodel
 ## How to use
 
 Download all files from this repository to the device  
-### 1. Install packages    
+### 1. Run python demo  
 
 #### 1. Install voxcpm axinfer package  
 ```
@@ -51,9 +51,25 @@ pip3 install modelscope
 modelscope download --model iic/speech_zipenhancer_ans_multiloss_16k_base --local_dir iic/speech_zipenhancer_ans_multiloss_16k_base
 ```
 
-### 2. Run on Axera Device    
+#### 3. Run on Axera Device    
 Go to the root directory of this project.  run:  
 ```
 python3 run_ax650.py
 ```
 
+### 2. Run c++ demo  
+
+#### 1. Install transformers
+```
+pip3 install transformers>=4.56.2
+```
+
+#### 2. Start tokenizer server  
+```
+python3 tokenizer.py --port 9999
+```
+
+#### 3. Run c++ demo  
+```
+bash run_ax650.sh
+```

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feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p64_l0_together.axmodel → axmodels/stop_predictor.onnx

@@ -1,3 +1,3 @@
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-oid sha256:1b3cbfa112705f4733ec8c279e33e55ac651e32da50508f690ea7ae21d5a3faa
-size 17555731
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+size 4207308

feat_decoder_estimator_decoder-axmodels/{MiniCPMForCausalLM_p64_l1_together.axmodel → MiniCPMForCausalLM_p5_l0_together.axmodel}

@@ -1,3 +1,3 @@
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-oid sha256:4736cb328473c1d4c107c8016ff1cf2a272d83a065290fe200e018582408a7f6
-size 17555731
+oid sha256:5aa16bbb9e750bbc1640054f061d894f62661f4b201913fbefcb5f6e185cd59f
+size 16718702

feat_decoder_estimator_decoder-axmodels/{MiniCPMForCausalLM_p64_l2_together.axmodel → MiniCPMForCausalLM_p5_l1_together.axmodel}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:24b49ea2d05791c2d98ad7302f9edc28ab29ca04f71205d7d9d15ba8855dca5c
-size 17555731
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+size 16718702

feat_decoder_estimator_decoder-axmodels/{MiniCPMForCausalLM_p64_l3_together.axmodel → MiniCPMForCausalLM_p5_l2_together.axmodel}

@@ -1,3 +1,3 @@
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-oid sha256:5ee811d1c91b8b1c8fe58542a465385a5d52e27ada56b59d1df30200d5c518f7
-size 17555731
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+size 16718702

feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_p5_l3_together.axmodel

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+size 16718702

feat_decoder_estimator_decoder-axmodels/MiniCPMForCausalLM_post.axmodel

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 version https://git-lfs.github.com/spec/v1
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-size 37570
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+size 37558

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l0_together.axmodel

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+version https://git-lfs.github.com/spec/v1
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+size 16695054

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l1_together.axmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1fedbd5e1c316695931347752e4500df9939bd36e6cd21a8e640f66395a0d00
+size 16695054

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l2_together.axmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d0570b6522c5f2e533345ea41ecfec3b7d7389f7557729fa7441494373c3920b
+size 16695054

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p3_l3_together.axmodel

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ca8a920dcbb282dd266e2aa69edd2c83e3b9510f31bdf597dafa18e8204c1ee
+size 16695054

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p64_l0_together.axmodel

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-size 17555739

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p64_l1_together.axmodel

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-oid sha256:95e8552843ab0a5c36ad73610be60d1327b8f5916772664ab312afcd5b34d546
-size 17555739

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p64_l2_together.axmodel

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-version https://git-lfs.github.com/spec/v1
-oid sha256:4c180c640df058af3a4393644b6d72cff1ac5024a1db1bdeb7a3a813fa8b120b
-size 17555739

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_p64_l3_together.axmodel

@@ -1,3 +0,0 @@
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-oid sha256:2ac9fddbe124f27719432ace6b335957cf990f19299e5755bfe85e74d7ecbf66
-size 17555739

feat_encoder_encoder-axmodels/MiniCPMForCausalLM_post.axmodel

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b738b68133f381b964a325d79e11b93f50831f21eb84c304ffaa9f18eec80821
-size 37570
+oid sha256:d51ee039826ff233cae480c5d3562bc65058d2fe3649dc0660b8411337648182
+size 37558

main_ax650

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c81f93c3b1089c2279c98af1019d5e852e8a8ffe0f23b577d2960e20be478ff5
+size 6718128

onnxruntime-aarch64-none-gnu-1.16.0/lib/cmake/onnxruntime/onnxruntimeConfig.cmake

@@ -0,0 +1,26 @@
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was PROJECT_CONFIG_FILE                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+macro(check_required_components _NAME)
+  foreach(comp ${${_NAME}_FIND_COMPONENTS})
+    if(NOT ${_NAME}_${comp}_FOUND)
+      if(${_NAME}_FIND_REQUIRED_${comp})
+        set(${_NAME}_FOUND FALSE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+####################################################################################
+include("${CMAKE_CURRENT_LIST_DIR}/onnxruntimeTargets.cmake")

onnxruntime-aarch64-none-gnu-1.16.0/lib/cmake/onnxruntime/onnxruntimeConfigVersion.cmake

@@ -0,0 +1,65 @@
+# This is a basic version file for the Config-mode of find_package().
+# It is used by write_basic_package_version_file() as input file for configure_file()
+# to create a version-file which can be installed along a config.cmake file.
+#
+# The created file sets PACKAGE_VERSION_EXACT if the current version string and
+# the requested version string are exactly the same and it sets
+# PACKAGE_VERSION_COMPATIBLE if the current version is >= requested version,
+# but only if the requested major version is the same as the current one.
+# The variable CVF_VERSION must be set before calling configure_file().
+
+
+set(PACKAGE_VERSION "1.16.0")
+
+if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
+  set(PACKAGE_VERSION_COMPATIBLE FALSE)
+else()
+
+  if("1.16.0" MATCHES "^([0-9]+)\\.")
+    set(CVF_VERSION_MAJOR "${CMAKE_MATCH_1}")
+    if(NOT CVF_VERSION_MAJOR VERSION_EQUAL 0)
+      string(REGEX REPLACE "^0+" "" CVF_VERSION_MAJOR "${CVF_VERSION_MAJOR}")
+    endif()
+  else()
+    set(CVF_VERSION_MAJOR "1.16.0")
+  endif()
+
+  if(PACKAGE_FIND_VERSION_RANGE)
+    # both endpoints of the range must have the expected major version
+    math (EXPR CVF_VERSION_MAJOR_NEXT "${CVF_VERSION_MAJOR} + 1")
+    if (NOT PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
+        OR ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX_MAJOR STREQUAL CVF_VERSION_MAJOR)
+          OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND NOT PACKAGE_FIND_VERSION_MAX VERSION_LESS_EQUAL CVF_VERSION_MAJOR_NEXT)))
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    elseif(PACKAGE_FIND_VERSION_MIN_MAJOR STREQUAL CVF_VERSION_MAJOR
+        AND ((PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "INCLUDE" AND PACKAGE_VERSION VERSION_LESS_EQUAL PACKAGE_FIND_VERSION_MAX)
+        OR (PACKAGE_FIND_VERSION_RANGE_MAX STREQUAL "EXCLUDE" AND PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION_MAX)))
+      set(PACKAGE_VERSION_COMPATIBLE TRUE)
+    else()
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    endif()
+  else()
+    if(PACKAGE_FIND_VERSION_MAJOR STREQUAL CVF_VERSION_MAJOR)
+      set(PACKAGE_VERSION_COMPATIBLE TRUE)
+    else()
+      set(PACKAGE_VERSION_COMPATIBLE FALSE)
+    endif()
+
+    if(PACKAGE_FIND_VERSION STREQUAL PACKAGE_VERSION)
+      set(PACKAGE_VERSION_EXACT TRUE)
+    endif()
+  endif()
+endif()
+
+
+# if the installed or the using project don't have CMAKE_SIZEOF_VOID_P set, ignore it:
+if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "" OR "8" STREQUAL "")
+  return()
+endif()
+
+# check that the installed version has the same 32/64bit-ness as the one which is currently searching:
+if(NOT CMAKE_SIZEOF_VOID_P STREQUAL "8")
+  math(EXPR installedBits "8 * 8")
+  set(PACKAGE_VERSION "${PACKAGE_VERSION} (${installedBits}bit)")
+  set(PACKAGE_VERSION_UNSUITABLE TRUE)
+endif()

onnxruntime-aarch64-none-gnu-1.16.0/lib/cmake/onnxruntime/onnxruntimeTargets-release.cmake

@@ -0,0 +1,19 @@
+#----------------------------------------------------------------
+# Generated CMake target import file for configuration "Release".
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Import target "onnxruntime::onnxruntime" for configuration "Release"
+set_property(TARGET onnxruntime::onnxruntime APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
+set_target_properties(onnxruntime::onnxruntime PROPERTIES
+  IMPORTED_LOCATION_RELEASE "${_IMPORT_PREFIX}/lib/libonnxruntime.so.1.16.0"
+  IMPORTED_SONAME_RELEASE "libonnxruntime.so.1.16.0"
+  )
+
+list(APPEND _cmake_import_check_targets onnxruntime::onnxruntime )
+list(APPEND _cmake_import_check_files_for_onnxruntime::onnxruntime "${_IMPORT_PREFIX}/lib/libonnxruntime.so.1.16.0" )
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)

onnxruntime-aarch64-none-gnu-1.16.0/lib/cmake/onnxruntime/onnxruntimeTargets.cmake

@@ -0,0 +1,103 @@
+# Generated by CMake
+
+if("${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}" LESS 2.8)
+   message(FATAL_ERROR "CMake >= 2.8.0 required")
+endif()
+if(CMAKE_VERSION VERSION_LESS "2.8.3")
+   message(FATAL_ERROR "CMake >= 2.8.3 required")
+endif()
+cmake_policy(PUSH)
+cmake_policy(VERSION 2.8.3...3.24)
+#----------------------------------------------------------------
+# Generated CMake target import file.
+#----------------------------------------------------------------
+
+# Commands may need to know the format version.
+set(CMAKE_IMPORT_FILE_VERSION 1)
+
+# Protect against multiple inclusion, which would fail when already imported targets are added once more.
+set(_cmake_targets_defined "")
+set(_cmake_targets_not_defined "")
+set(_cmake_expected_targets "")
+foreach(_cmake_expected_target IN ITEMS onnxruntime::onnxruntime)
+  list(APPEND _cmake_expected_targets "${_cmake_expected_target}")
+  if(TARGET "${_cmake_expected_target}")
+    list(APPEND _cmake_targets_defined "${_cmake_expected_target}")
+  else()
+    list(APPEND _cmake_targets_not_defined "${_cmake_expected_target}")
+  endif()
+endforeach()
+unset(_cmake_expected_target)
+if(_cmake_targets_defined STREQUAL _cmake_expected_targets)
+  unset(_cmake_targets_defined)
+  unset(_cmake_targets_not_defined)
+  unset(_cmake_expected_targets)
+  unset(CMAKE_IMPORT_FILE_VERSION)
+  cmake_policy(POP)
+  return()
+endif()
+if(NOT _cmake_targets_defined STREQUAL "")
+  string(REPLACE ";" ", " _cmake_targets_defined_text "${_cmake_targets_defined}")
+  string(REPLACE ";" ", " _cmake_targets_not_defined_text "${_cmake_targets_not_defined}")
+  message(FATAL_ERROR "Some (but not all) targets in this export set were already defined.\nTargets Defined: ${_cmake_targets_defined_text}\nTargets not yet defined: ${_cmake_targets_not_defined_text}\n")
+endif()
+unset(_cmake_targets_defined)
+unset(_cmake_targets_not_defined)
+unset(_cmake_expected_targets)
+
+
+# Compute the installation prefix relative to this file.
+get_filename_component(_IMPORT_PREFIX "${CMAKE_CURRENT_LIST_FILE}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+get_filename_component(_IMPORT_PREFIX "${_IMPORT_PREFIX}" PATH)
+if(_IMPORT_PREFIX STREQUAL "/")
+  set(_IMPORT_PREFIX "")
+endif()
+
+# Create imported target onnxruntime::onnxruntime
+add_library(onnxruntime::onnxruntime SHARED IMPORTED)
+
+set_target_properties(onnxruntime::onnxruntime PROPERTIES
+  INTERFACE_COMPILE_DEFINITIONS "NSYNC_ATOMIC_CPP11"
+  INTERFACE_INCLUDE_DIRECTORIES "${_IMPORT_PREFIX}/include/onnxruntime"
+)
+
+# Load information for each installed configuration.
+file(GLOB _cmake_config_files "${CMAKE_CURRENT_LIST_DIR}/onnxruntimeTargets-*.cmake")
+foreach(_cmake_config_file IN LISTS _cmake_config_files)
+  include("${_cmake_config_file}")
+endforeach()
+unset(_cmake_config_file)
+unset(_cmake_config_files)
+
+# Cleanup temporary variables.
+set(_IMPORT_PREFIX)
+
+# Loop over all imported files and verify that they actually exist
+foreach(_cmake_target IN LISTS _cmake_import_check_targets)
+  foreach(_cmake_file IN LISTS "_cmake_import_check_files_for_${_cmake_target}")
+    if(NOT EXISTS "${_cmake_file}")
+      message(FATAL_ERROR "The imported target \"${_cmake_target}\" references the file
+   \"${_cmake_file}\"
+but this file does not exist.  Possible reasons include:
+* The file was deleted, renamed, or moved to another location.
+* An install or uninstall procedure did not complete successfully.
+* The installation package was faulty and contained
+   \"${CMAKE_CURRENT_LIST_FILE}\"
+but not all the files it references.
+")
+    endif()
+  endforeach()
+  unset(_cmake_file)
+  unset("_cmake_import_check_files_for_${_cmake_target}")
+endforeach()
+unset(_cmake_target)
+unset(_cmake_import_check_targets)
+
+# This file does not depend on other imported targets which have
+# been exported from the same project but in a separate export set.
+
+# Commands beyond this point should not need to know the version.
+set(CMAKE_IMPORT_FILE_VERSION)
+cmake_policy(POP)

onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c3547fa67e5c721bf8652b40878ac37fbb89d3b98f3bd45cee856a7db51dea5
+size 18085192

onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime.so.1.16.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c3547fa67e5c721bf8652b40878ac37fbb89d3b98f3bd45cee856a7db51dea5
+size 18085192

onnxruntime-aarch64-none-gnu-1.16.0/lib/libonnxruntime_providers_shared.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75f61040045e021e9385081db84cf538e9581bcd64f9077abdbb2d8a4ec6d989
+size 9992

onnxruntime-aarch64-none-gnu-1.16.0/lib/pkgconfig/libonnxruntime.pc

@@ -0,0 +1,13 @@
+prefix=/home/arno/workspace/projects/onnxruntime/build/ax650/install
+bindir=${prefix}/bin
+mandir=${prefix}/
+docdir=${prefix}/
+libdir=${prefix}/lib
+includedir=${prefix}/include/onnxruntime
+
+Name: onnxruntime
+Description: ONNX runtime
+URL: https://github.com/microsoft/onnxruntime
+Version: 1.16.0
+Libs: -L${libdir} -lonnxruntime
+Cflags: -I${includedir}

run_ax650.py

@@ -14,8 +14,8 @@ t1 = time.time()
 chunks = []
 for chunk in model.generate_streaming(
     text = "Streaming text to speech is easy with VoxCPM!",
-    prompt_wav_path="assets/en_man1.mp3",      # optional: path to a prompt speech for voice cloning
-    prompt_text="Because he has zero capacity to respond to the two and a half hour",          # optional: reference text
+    prompt_wav_path="assets/en_woman1.mp3",      # optional: path to a prompt speech for voice cloning
+    prompt_text="But many of these southern girls have the same trouble, said Holly.",          # optional: reference text
     cfg_value=2.0,             # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
     inference_timesteps=10,   # LocDiT inference timesteps, higher for better result, lower for fast speed
     normalize=True,           # enable external TN tool

run_ax650.sh

@@ -0,0 +1,18 @@
+BASE=./
+export LD_LIBRARY_PATH=./onnxruntime-aarch64-none-gnu-1.16.0/lib/:$LD_LIBRARY_PATH
+
+rm output*.wav
+./main_ax650 \
+--dir_axmodels $BASE/axmodels \
+--dir_base_lm  $BASE/base_lm-axmodels \
+--dir_residual_lm $BASE/residual_lm-axmodels \
+--dir_feat_encoder $BASE/feat_encoder_encoder-axmodels \
+--dir_decoder_estimator $BASE/feat_decoder_estimator_decoder-axmodels \
+--url_tokenizer "http://127.0.0.1:9999" \
+--n_timesteps 10 \
+--continue 0 \
+--prompt_wav_path assets/en_woman1.mp3 \
+--prompt_text "But many of these southern girls have the same trouble, said Holly." \
+--text "Streaming text to speech is easy with VoxCPM"
+
+chmod 777 output*.wav

tokenizer.py

@@ -0,0 +1,222 @@
+from transformers import LlamaTokenizerFast, PreTrainedTokenizer
+from http.server import HTTPServer, BaseHTTPRequestHandler
+import json
+import argparse
+from typing import List
+
+def mask_multichar_chinese_tokens(tokenizer: PreTrainedTokenizer):
+    """Create a tokenizer wrapper that converts multi-character Chinese tokens to single characters.
+    
+    This function creates a wrapper around the provided tokenizer that automatically
+    splits multi-character Chinese tokens into individual characters. This is useful
+    for ensuring consistent tokenization of Chinese text.
+    
+    Args:
+        tokenizer: The base tokenizer to wrap
+        
+    Returns:
+        A CharTokenizerWrapper instance that handles multi-character Chinese tokens
+        
+    Example:
+        >>> from transformers import LlamaTokenizerFast
+        >>> tokenizer = LlamaTokenizerFast.from_pretrained("path/to/tokenizer")
+        >>> wrapped_tokenizer = mask_multichar_chinese_tokens(tokenizer)
+        >>> tokens = wrapped_tokenizer("你好世界")
+    """
+    # Pre-compute multi-character tokens (length >= 2, pure Chinese characters)
+    multichar_tokens = {
+        token for token in tokenizer.vocab.keys() 
+        if len(token) >= 2 and all("\u4e00" <= c <= "\u9fff" for c in token)
+    }
+
+    class CharTokenizerWrapper:
+        """Wrapper class for tokenizers that handles multi-character Chinese tokens.
+        
+        This wrapper automatically splits multi-character Chinese tokens into
+        individual characters while preserving the original tokenizer's interface.
+        """
+        
+        def __init__(self, base_tokenizer: PreTrainedTokenizer) -> None:
+            """Initialize the wrapper with a base tokenizer.
+            
+            Args:
+                base_tokenizer: The tokenizer to wrap
+            """
+            self.tokenizer = base_tokenizer
+            self.multichar_tokens = multichar_tokens
+
+        def tokenize(self, text: str, **kwargs) -> List[str]:
+            """Tokenize text and split multi-character Chinese tokens into single characters.
+            
+            Args:
+                text: Input text to tokenize
+                **kwargs: Additional arguments passed to the base tokenizer
+                
+            Returns:
+                List of processed tokens with multi-character Chinese tokens split
+                
+            Example:
+                >>> wrapper = CharTokenizerWrapper(tokenizer)
+                >>> tokens = wrapper.tokenize("你好世界")
+                >>> # Returns ["你", "好", "世", "界"] instead of ["你好", "世界"]
+            """
+            if not isinstance(text, str):
+                raise TypeError(f"Expected string input, got {type(text)}")
+                
+            tokens = self.tokenizer.tokenize(text, **kwargs)
+            processed = []
+            
+            for token in tokens:
+                # Remove possible subword prefix
+                clean_token = token.replace("▁", "")
+
+                if clean_token in self.multichar_tokens:
+                    # Split multi-character token into single characters
+                    chars = list(clean_token)
+                    processed.extend(chars)
+                else:
+                    processed.append(token)
+                    
+            return processed
+
+        def __call__(self, text: str, **kwargs) -> List[int]:
+            """Call the tokenizer and return token IDs.
+            
+            This method provides the same interface as the original tokenizer
+            but with multi-character Chinese token handling.
+            
+            Args:
+                text: Input text to tokenize
+                **kwargs: Additional arguments passed to the base tokenizer
+                
+            Returns:
+                List of token IDs
+                
+            Raises:
+                TypeError: If input is not a string
+                ValueError: If tokenization fails
+            """
+            try:
+                tokens = self.tokenize(text, **kwargs)
+                result = self.tokenizer.convert_tokens_to_ids(tokens)
+                return result
+            except Exception as e:
+                raise ValueError(f"Tokenization failed: {str(e)}") from e
+
+    return CharTokenizerWrapper(tokenizer)
+
+class Tokenizer_Http():
+
+    def __init__(self):
+        tokenizer = LlamaTokenizerFast.from_pretrained("./VoxCPM-0.5B")
+        self.tokenizer = mask_multichar_chinese_tokens(tokenizer)
+
+    def encode(self, prompt):
+    
+        token_ids = self.tokenizer(prompt)
+        return token_ids
+
+    # @property
+    # def bos_id(self):
+    #     return self.tokenizer.bos_token_id
+
+    @property
+    def eos_id(self):
+        return 1773
+    
+    # @property
+    # def bos_token(self):
+    #     return self.tokenizer.bos_token
+
+    @property
+    def eos_token(self):
+        return "<|eot_id|>"
+
+
+tokenizer = Tokenizer_Http()
+
+# print(tokenizer.bos_id, tokenizer.bos_token, tokenizer.eos_id, tokenizer.eos_token)
+print(tokenizer.encode("hello world"))
+
+
+class Request(BaseHTTPRequestHandler):
+    #通过类继承，新定义类
+    timeout = 5
+    server_version = 'Apache'
+
+    def do_GET(self):
+        print(self.path)
+        #在新类中定义get的内容（当客户端向该服务端使用get请求时，本服务端将如下运行）
+        self.send_response(200)
+        self.send_header("type", "get")  #设置响应头，可省略或设置多个
+        self.end_headers()
+
+        if self.path == '/bos_id':
+            bos_id = tokenizer.bos_id
+            # print(bos_id)
+            # to json
+            if bos_id is None:
+                msg = json.dumps({'bos_id': -1})
+            else:
+                msg = json.dumps({'bos_id': bos_id})
+        elif self.path == '/eos_id':
+            eos_id = tokenizer.eos_id
+            if eos_id is None:
+                msg = json.dumps({'eos_id': -1})
+            else:
+                msg = json.dumps({'eos_id': eos_id})
+        else:
+            msg = 'error'
+
+        print(msg)
+        msg = str(msg).encode()  #转为str再转为byte格式
+
+        self.wfile.write(msg)  #将byte格式的信息返回给客户端
+
+    def do_POST(self):
+        #在新类中定义post的内容（当客户端向该服务端使用post请求时，本服务端将如下运行）
+        data = self.rfile.read(int(
+            self.headers['content-length']))  #获取从客户端传入的参数（byte格式）
+        data = data.decode()  #将byte格式转为str格式
+
+        self.send_response(200)
+        self.send_header("type", "post")  #设置响应头，可省略或设置多个
+        self.end_headers()
+
+        if self.path == '/encode':
+            req = json.loads(data)
+            prompt = req['text']
+
+            token_ids = tokenizer.encode(prompt)
+            if token_ids is None:
+                msg = json.dumps({'token_ids': -1})
+            else:
+                msg = json.dumps({'token_ids': token_ids})
+
+        # elif self.path == '/decode':
+        #     req = json.loads(data)
+        #     token_ids = req['token_ids']
+        #     text = tokenizer.decode(token_ids)
+        #     if text is None:
+        #         msg = json.dumps({'text': ""})
+        #     else:
+        #         msg = json.dumps({'text': text})
+        else:
+            msg = 'error'
+        print(msg)
+        msg = str(msg).encode()  #转为str再转为byte格式
+
+        self.wfile.write(msg)  #将byte格式的信息返回给客户端
+
+
+if __name__ == "__main__":
+
+    args = argparse.ArgumentParser()
+    args.add_argument('--host', type=str, default='localhost')
+    args.add_argument('--port', type=int, default=9999)
+    args = args.parse_args()
+
+    host = (args.host, args.port)  #设定地址与端口号，'localhost'等价于'127.0.0.1'
+    print('http://%s:%s' % host)
+    server = HTTPServer(host, Request)  #根据地址端口号和新定义的类，创建服务器实例
+    server.serve_forever()  #开启服务