fix(kokoro): voice loading, Synth method selection, padding fixes

Voice loading:
- Read all rows from voice file instead of truncating to kMaxInputTokens.
  Voice files (hexgrad/Kokoro-82M) have 510 rows; upstream discards 382.
- Changed voice_ from fixed std::array to std::vector, sized from file.
- voiceID: three-way min(phonemes-1, dpTokens-1, voice_.size()-1) to
  prevent OOB. Upstream had a latent OOB with voiceID=noTokens on a
  128-element array.

Synthesizer method selection:
- Discover forward_N methods at construction, same pattern DurationPredictor
  already uses. Falls back to "forward" for older/single-method models.
- Uses execute() instead of forward() for named method dispatch.

Padding fixes:
- Pad indices to inputDurationLimit before Synthesizer to prevent XNNPACK
  shape mismatch on repeated calls with varying duration predictions.
- When DP and Synth use same token count (common case), pass DP tensor
  directly to Synth instead of copying (~320KB save).

Perf:
- Use resize() for silence padding instead of allocating temp vectors.
- Move-capture audio in streaming callback instead of copying.

Author: yocontra

packages/react-native-executorch/common/rnexecutorch/models/text_to_speech/kokoro/DurationPredictor.h

@@ -1,5 +1,6 @@
 #pragma once
 
+#include <algorithm>
 #include <memory>
 #include <span>
 #include <string>
@@ -44,6 +45,14 @@ class DurationPredictor : public BaseModel {
   // Returns maximum supported amount of input tokens.
   size_t getTokensLimit() const;
 
+  // Returns the token count of the forward method that would be selected
+  // for a given input size. E.g., input 37 -> returns 64 (forward_64).
+  size_t getMethodTokenCount(size_t inputSize) const {
+    auto it = std::ranges::find_if(forwardMethods_,
+        [inputSize](const auto &e) { return e.second >= inputSize; });
+    return (it != forwardMethods_.end()) ? it->second : forwardMethods_.back().second;
+  }
+
 private:
   // Helper function - duration scalling
   // Performs integer scaling on the durations tensor to ensure the sum of

packages/react-native-executorch/common/rnexecutorch/models/text_to_speech/kokoro/Kokoro.cpp

@@ -39,38 +39,39 @@ Kokoro::Kokoro(const std::string &lang, const std::string &taggerDataSource,
 }
 
 void Kokoro::loadVoice(const std::string &voiceSource) {
-  constexpr size_t rows = static_cast<size_t>(constants::kMaxInputTokens);
-  constexpr size_t cols = static_cast<size_t>(constants::kVoiceRefSize); // 256
-  const size_t expectedCount = rows * cols;
-  const std::streamsize expectedBytes =
-      static_cast<std::streamsize>(expectedCount * sizeof(float));
+  constexpr size_t cols = static_cast<size_t>(constants::kVoiceRefSize);
+  constexpr size_t bytesPerRow = cols * sizeof(float);
 
   std::ifstream in(voiceSource, std::ios::binary);
   if (!in) {
     throw RnExecutorchError(RnExecutorchErrorCode::FileReadFailed,
-                            "[Kokoro::loadSingleVoice]: cannot open file: " +
+                            "[Kokoro::loadVoice]: cannot open file: " +
                                 voiceSource);
   }
 
-  // Check the file size
+  // Determine number of rows from file size
   in.seekg(0, std::ios::end);
-  const std::streamsize fileSize = in.tellg();
+  const auto fileSize = static_cast<size_t>(in.tellg());
   in.seekg(0, std::ios::beg);
-  if (fileSize < expectedBytes) {
+
+  if (fileSize < bytesPerRow) {
     throw RnExecutorchError(
         RnExecutorchErrorCode::FileReadFailed,
-        "[Kokoro::loadSingleVoice]: file too small: expected at least " +
-            std::to_string(expectedBytes) + " bytes, got " +
+        "[Kokoro::loadVoice]: file too small: need at least " +
+            std::to_string(bytesPerRow) + " bytes for one row, got " +
             std::to_string(fileSize));
   }
 
-  // Read [rows, 1, cols] as contiguous floats directly into voice_
-  // ([rows][cols])
-  if (!in.read(reinterpret_cast<char *>(voice_.data()->data()),
-               expectedBytes)) {
+  const size_t rows = fileSize / bytesPerRow;
+  const auto readBytes = static_cast<std::streamsize>(rows * bytesPerRow);
+
+  // Resize voice vector to hold all rows from the file
+  voice_.resize(rows);
+
+  if (!in.read(reinterpret_cast<char *>(voice_.data()->data()), readBytes)) {
     throw RnExecutorchError(
         RnExecutorchErrorCode::FileReadFailed,
-        "[Kokoro::loadSingleVoice]: failed to read voice weights");
+        "[Kokoro::loadVoice]: failed to read voice weights");
   }
 }
 
@@ -108,10 +109,11 @@ void Kokoro::streamFromPhonemesImpl(
     std::shared_ptr<jsi::Function> callback) {
   auto nativeCallback = [this, callback](const std::vector<float> &audioVec) {
     if (this->isStreaming_) {
-      this->callInvoker_->invokeAsync([callback, audioVec](jsi::Runtime &rt) {
-        callback->call(rt,
-                       rnexecutorch::jsi_conversion::getJsiValue(audioVec, rt));
-      });
+      this->callInvoker_->invokeAsync(
+          [callback, audioVec = std::move(audioVec)](jsi::Runtime &rt) {
+            callback->call(
+                rt, rnexecutorch::jsi_conversion::getJsiValue(audioVec, rt));
+          });
     }
   };
 
@@ -149,7 +151,7 @@ void Kokoro::streamFromPhonemesImpl(
         audioPart.size() + pauseMs * constants::kSamplesPerMilisecond, 0.F);
 
     // Push the audio right away to the JS side
-    nativeCallback(audioPart);
+    nativeCallback(std::move(audioPart));
   }
 
   isStreaming_ = false;
@@ -209,41 +211,62 @@ std::vector<float> Kokoro::synthesize(const std::u32string &phonemes,
     return {};
   }
 
-  // Clamp the input to not go beyond number of input token limits
-  // Note that 2 tokens are always reserved for pre- and post-fix padding,
-  // so we effectively take at most (maxNoInputTokens_ - 2) tokens.
-  size_t noTokens = std::clamp(phonemes.size() + 2, constants::kMinInputTokens,
+  // Clamp token count: phonemes + 2 padding tokens (leading + trailing zero)
+  size_t dpTokens = std::clamp(phonemes.size() + 2,
+                               constants::kMinInputTokens,
                                context_.inputTokensLimit);
 
-  // Map phonemes to tokens
-  const auto tokens = utils::tokenize(phonemes, {noTokens});
+  // Map phonemes to tokens, padded to dpTokens
+  auto tokens = utils::tokenize(phonemes, {dpTokens});
 
   // Select the appropriate voice vector
-  size_t voiceID = std::min(phonemes.size() - 1, noTokens);
+  size_t voiceID = std::min({phonemes.size() - 1, dpTokens - 1,
+                             voice_.size() - 1});
   auto &voice = voice_[voiceID];
 
-  // Initialize text mask
-  // Exclude all the paddings apart from first and last one.
-  size_t realInputLength = std::min(phonemes.size() + 2, noTokens);
-  std::vector<uint8_t> textMask(noTokens, false);
+  // Initialize text mask for DP
+  size_t realInputLength = std::min(phonemes.size() + 2, dpTokens);
+  std::vector<uint8_t> textMask(dpTokens, false);
   std::fill(textMask.begin(), textMask.begin() + realInputLength, true);
 
   // Inference 1 - DurationPredictor
-  // The resulting duration vector is already scalled at this point
   auto [d, indices, effectiveDuration] = durationPredictor_.generate(
       std::span(tokens),
       std::span(reinterpret_cast<bool *>(textMask.data()), textMask.size()),
       std::span(voice).last(constants::kVoiceRefHalfSize), speed);
 
+  // --- Synthesizer phase ---
+  // The Synthesizer may have different method sizes than the DP.
+  // Pad all inputs to the Synthesizer's selected method size.
+  size_t synthTokens = synthesizer_.getMethodTokenCount(dpTokens);
+  size_t dCols = d.sizes().back(); // 640
+
+  // Pad tokens and textMask to synthTokens (no-op when synthTokens == dpTokens)
+  tokens.resize(synthTokens, 0);
+  textMask.resize(synthTokens, false);
+
+  // Pad indices to the maximum duration limit
+  indices.resize(context_.inputDurationLimit, 0);
+
+  // Prepare duration data for Synthesizer.
+  // When sizes match, pass the DP tensor directly to avoid a 320KB copy.
+  size_t durSize = synthTokens * dCols;
+  std::vector<float> durPadded;
+  float *durPtr;
+  if (synthTokens == dpTokens) {
+    durPtr = d.mutable_data_ptr<float>();
+  } else {
+    durPadded.resize(durSize, 0.0f);
+    std::copy_n(d.const_data_ptr<float>(), dpTokens * dCols, durPadded.data());
+    durPtr = durPadded.data();
+  }
+
   // Inference 2 - Synthesizer
   auto decoding = synthesizer_.generate(
       std::span(tokens),
       std::span(reinterpret_cast<bool *>(textMask.data()), textMask.size()),
       std::span(indices),
-      // Note that we reduce the size of d tensor to match the initial number of
-      // input tokens
-      std::span<float>(d.mutable_data_ptr<float>(),
-                       noTokens * d.sizes().back()),
+      std::span<float>(durPtr, durSize),
       std::span(voice));
   auto audioTensor = decoding->at(0).toTensor();
 
@@ -254,9 +277,7 @@ std::vector<float> Kokoro::synthesize(const std::u32string &phonemes,
   auto croppedAudio =
       utils::stripAudio(audio, paddingMs * constants::kSamplesPerMilisecond);
 
-  std::vector<float> result(croppedAudio.begin(), croppedAudio.end());
-
-  return result;
+  return {croppedAudio.begin(), croppedAudio.end()};
 }
 
 std::size_t Kokoro::getMemoryLowerBound() const noexcept {

packages/react-native-executorch/common/rnexecutorch/models/text_to_speech/kokoro/Kokoro.h

@@ -75,12 +75,9 @@ class Kokoro {
   DurationPredictor durationPredictor_;
   Synthesizer synthesizer_;
 
-  // Voice array
-  // There is a separate voice vector for each of the possible numbers of input
-  // tokens.
-  std::array<std::array<float, constants::kVoiceRefSize>,
-             constants::kMaxInputTokens>
-      voice_;
+  // Voice array — dynamically sized to match the voice file.
+  // Each row is a style vector for a given input token count.
+  std::vector<std::array<float, constants::kVoiceRefSize>> voice_;
 
   // Extra control variables
   bool isStreaming_ = false;

packages/react-native-executorch/common/rnexecutorch/models/text_to_speech/kokoro/Synthesizer.cpp

@@ -13,18 +13,34 @@ Synthesizer::Synthesizer(const std::string &modelSource,
                          const Context &modelContext,
                          std::shared_ptr<react::CallInvoker> callInvoker)
     : BaseModel(modelSource, callInvoker), context_(modelContext) {
-  const auto inputTensors = getAllInputShapes("forward");
+  // Discover all forward methods (forward, forward_8, forward_32, etc.)
+  auto availableMethods = module_->method_names();
+  if (availableMethods.ok()) {
+    const auto &names = *availableMethods;
+    for (const auto &name : names) {
+      if (name.rfind("forward", 0) == 0) {
+        const auto inputTensors = getAllInputShapes(name);
+        CHECK_SIZE(inputTensors, 5);
+        CHECK_SIZE(inputTensors[0], 2);
+        CHECK_SIZE(inputTensors[1], 2);
+        CHECK_SIZE(inputTensors[2], 1);
+        size_t inputSize = inputTensors[0][1];
+        forwardMethods_.emplace_back(name, inputSize);
+      }
+    }
+    std::stable_sort(forwardMethods_.begin(), forwardMethods_.end(),
+                     [](const auto &a, const auto &b) { return a.second < b.second; });
+  }
 
-  // Perform checks to validate model's compatibility with native code
-  CHECK_SIZE(inputTensors, 5);
-  CHECK_SIZE(
-      inputTensors[0],
-      2); // input tokens must be of shape {1, T}, where T is number of tokens
-  CHECK_SIZE(
-      inputTensors[1],
-      2); // text mask must be of shape {1, T}, where T is number of tokens
-  CHECK_SIZE(inputTensors[2],
-             1); // indices must be of shape {D}, where D is a maximum duration
+  // Fallback: if no methods discovered, validate "forward" directly
+  if (forwardMethods_.empty()) {
+    const auto inputTensors = getAllInputShapes("forward");
+    CHECK_SIZE(inputTensors, 5);
+    CHECK_SIZE(inputTensors[0], 2);
+    CHECK_SIZE(inputTensors[1], 2);
+    CHECK_SIZE(inputTensors[2], 1);
+    forwardMethods_.emplace_back("forward", inputTensors[0][1]);
+  }
 }
 
 Result<std::vector<EValue>> Synthesizer::generate(std::span<const Token> tokens,
@@ -54,14 +70,19 @@ Result<std::vector<EValue>> Synthesizer::generate(std::span<const Token> tokens,
   auto voiceRefTensor = make_tensor_ptr({1, constants::kVoiceRefSize},
                                         ref_s.data(), ScalarType::Float);
 
-  // Execute the appropriate "forward_xyz" method, based on given method name
-  auto results = forward(
+  // Select appropriate forward method based on token count
+  auto it = std::find_if(forwardMethods_.begin(), forwardMethods_.end(),
+      [noTokens](const auto &entry) { return static_cast<int32_t>(entry.second) >= noTokens; });
+  std::string selectedMethod = (it != forwardMethods_.end()) ? it->first : forwardMethods_.back().first;
+
+  // Execute the selected forward method
+  auto results = execute(selectedMethod,
       {tokensTensor, textMaskTensor, indicesTensor, durTensor, voiceRefTensor});
 
   if (!results.ok()) {
     throw RnExecutorchError(
         RnExecutorchErrorCode::InvalidModelOutput,
-        "[Kokoro::Synthesizer] Failed to execute method forward"
+        "[Kokoro::Synthesizer] Failed to execute method " + selectedMethod +
         ", error: " +
             std::to_string(static_cast<uint32_t>(results.error())));
   }
@@ -72,13 +93,12 @@ Result<std::vector<EValue>> Synthesizer::generate(std::span<const Token> tokens,
 }
 
 size_t Synthesizer::getTokensLimit() const {
-  // Returns tokens input (shape {1, T}) second dim
-  return getInputShape("forward", 0)[1];
+  return forwardMethods_.empty() ? 0 : forwardMethods_.back().second;
 }
 
 size_t Synthesizer::getDurationLimit() const {
-  // Returns indices vector first dim (shape {D})
-  return getInputShape("forward", 2)[0];
+  if (forwardMethods_.empty()) return 0;
+  return getInputShape(forwardMethods_.back().first, 2)[0];
 }
 
 } // namespace rnexecutorch::models::text_to_speech::kokoro

packages/react-native-executorch/common/rnexecutorch/models/text_to_speech/kokoro/Synthesizer.h

@@ -1,5 +1,6 @@
 #pragma once
 
+#include <algorithm>
 #include <memory>
 #include <span>
 #include <string>
@@ -49,7 +50,17 @@ class Synthesizer : public BaseModel {
   size_t getTokensLimit() const;
   size_t getDurationLimit() const;
 
+  // Returns the token count of the forward method that would be selected
+  // for a given input size. E.g., input 37 -> returns 64 (forward_64).
+  size_t getMethodTokenCount(size_t inputSize) const {
+    auto it = std::ranges::find_if(forwardMethods_,
+        [inputSize](const auto &e) { return e.second >= inputSize; });
+    return (it != forwardMethods_.end()) ? it->second : forwardMethods_.back().second;
+  }
+
 private:
+  // Forward methods discovered at construction (e.g. forward_8, forward_64, forward_128)
+  std::vector<std::pair<std::string, size_t>> forwardMethods_;
   // Shared model context
   // A const reference to singleton in Kokoro.
   const Context &context_;