Export the English TTS model from MeloTTS (#1509)

@@ -40,7 +40,7 @@ jobs:
           name: test.wav
           path: scripts/melo-tts/test.wav
 
-      - name: Publish to huggingface
+      - name: Publish to huggingface (Chinese + English)
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         uses: nick-fields/retry@v3
@@ -61,14 +61,14 @@ jobs:
             git fetch
             git pull
             echo "pwd: $PWD"
-            ls -lh ../scripts/melo-tts
+            ls -lh ../scripts/melo-tts/zh_en
 
             rm -rf ./
 
-            cp -v ../scripts/melo-tts/*.onnx .
-            cp -v ../scripts/melo-tts/lexicon.txt .
-            cp -v ../scripts/melo-tts/tokens.txt .
-            cp -v ../scripts/melo-tts/README.md .
+            cp -v ../scripts/melo-tts/zh_en/*.onnx .
+            cp -v ../scripts/melo-tts/zh_en/lexicon.txt .
+            cp -v ../scripts/melo-tts/zh_en/tokens.txt .
+            cp -v ../scripts/melo-tts/zh_en/README.md .
 
             curl -SL -O https://raw.githubusercontent.com/myshell-ai/MeloTTS/main/LICENSE
 
@@ -102,6 +102,60 @@ jobs:
             tar cjvf $dst.tar.bz2 $dst
             rm -rf $dst
 
+      - name: Publish to huggingface (English)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        uses: nick-fields/retry@v3
+        with:
+          max_attempts: 20
+          timeout_seconds: 200
+          shell: bash
+          command: |
+            git config --global user.email "csukuangfj@gmail.com"
+            git config --global user.name "Fangjun Kuang"
+
+            rm -rf huggingface
+            export GIT_LFS_SKIP_SMUDGE=1
+            export GIT_CLONE_PROTECTION_ACTIVE=false
+
+            git clone https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/vits-melo-tts-en huggingface
+            cd huggingface
+            git fetch
+            git pull
+            echo "pwd: $PWD"
+            ls -lh ../scripts/melo-tts/en
+
+            rm -rf ./
+
+            cp -v ../scripts/melo-tts/en/*.onnx .
+            cp -v ../scripts/melo-tts/en/lexicon.txt .
+            cp -v ../scripts/melo-tts/en/tokens.txt .
+            cp -v ../scripts/melo-tts/en/README.md .
+
+            curl -SL -O https://raw.githubusercontent.com/myshell-ai/MeloTTS/main/LICENSE
+
+            git lfs track "*.onnx"
+            git add .
+
+            ls -lh
+
+            git status
+
+            git diff
+
+            git commit -m "add models"
+            git push https://csukuangfj:$HF_TOKEN@huggingface.co/csukuangfj/vits-melo-tts-en main || true
+
+            cd ..
+
+            rm -rf huggingface/.git*
+            dst=vits-melo-tts-en
+
+            mv huggingface $dst
+
+            tar cjvf $dst.tar.bz2 $dst
+            rm -rf $dst
+
       - name: Release
         uses: svenstaro/upload-release-action@v2
         with:

@@ -3,4 +3,5 @@
 Models in this directory are converted from
 https://github.com/myshell-ai/MeloTTS
 
-Note there is only a single female speaker in the model.
+Note there is only a single female speaker in the model for Chinese+English TTS.
+TTS model, whereas there are 5 female speakers in the model For English TTS.

+#!/usr/bin/env python3
+# This model exports the English-only TTS model.
+# It has 5 speakers.
+# {'EN-US': 0, 'EN-BR': 1, 'EN_INDIA': 2, 'EN-AU': 3, 'EN-Default': 4}
+
+from typing import Any, Dict
+
+import onnx
+import torch
+from melo.api import TTS
+from melo.text import language_id_map, language_tone_start_map
+from melo.text.chinese import pinyin_to_symbol_map
+from melo.text.english import eng_dict, refine_syllables
+from pypinyin import Style, lazy_pinyin, phrases_dict, pinyin_dict
+
+
+def generate_tokens(symbol_list):
+    with open("tokens.txt", "w", encoding="utf-8") as f:
+        for i, s in enumerate(symbol_list):
+            f.write(f"{s} {i}\n")
+
+
+def add_new_english_words(lexicon):
+    """
+    Args:
+      lexicon:
+        Please modify it in-place.
+    """
+
+    # Please have a look at
+    # https://github.com/myshell-ai/MeloTTS/blob/main/melo/text/cmudict.rep
+
+    # We give several examples below about how to add new words
+
+    # Example 1. Add a new word kaldi
+
+    # It does not contain the word kaldi in cmudict.rep
+    # so if we add the following line to cmudict.rep
+    #
+    #  KALDI K AH0 - L D IH0
+    #
+    # then we need to change the lexicon like below
+    lexicon["kaldi"] = [["K", "AH0"], ["L", "D", "IH0"]]
+    #
+    # K AH0 and L D IH0 are separated by a dash "-", so
+    # ["K", "AH0"] is a in list and ["L", "D", "IH0"] is in a separate list
+
+    # Note: Either kaldi or KALDI is fine. You can use either lowercase or
+    # uppercase or both
+
+    # Example 2. Add a new word SF
+    #
+    # If we add the following line to cmudict.rep
+    #
+    #  SF EH1 S - EH1 F
+    #
+    # to cmudict.rep, then we need to change the lexicon like below:
+    lexicon["SF"] = [["EH1", "S"], ["EH1", "F"]]
+
+    # Please add your new words here
+
+    # No need to return lexicon since it is changed in-place
+
+
+def generate_lexicon():
+    add_new_english_words(eng_dict)
+    with open("lexicon.txt", "w", encoding="utf-8") as f:
+        for word in eng_dict:
+            phones, tones = refine_syllables(eng_dict[word])
+            tones = [t + language_tone_start_map["EN"] for t in tones]
+            tones = [str(t) for t in tones]
+
+            phones = " ".join(phones)
+            tones = " ".join(tones)
+
+            f.write(f"{word.lower()} {phones} {tones}\n")
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, Any]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        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)
+
+
+class ModelWrapper(torch.nn.Module):
+    def __init__(self, model: "SynthesizerTrn"):
+        super().__init__()
+        self.model = model
+        self.lang_id = language_id_map[model.language]
+
+    def forward(
+        self,
+        x,
+        x_lengths,
+        tones,
+        sid,
+        noise_scale,
+        length_scale,
+        noise_scale_w,
+        max_len=None,
+    ):
+        """
+        Args:
+          x: A 1-D array of dtype np.int64. Its shape is (token_numbers,)
+          tones: A 1-D array of dtype np.int64. Its shape is (token_numbers,)
+          lang_id: A 1-D array of dtype np.int64. Its shape is (token_numbers,)
+          sid: an integer
+        """
+        bert = torch.zeros(x.shape[0], 1024, x.shape[1], dtype=torch.float32)
+        ja_bert = torch.zeros(x.shape[0], 768, x.shape[1], dtype=torch.float32)
+        lang_id = torch.zeros_like(x)
+        lang_id[:, 1::2] = self.lang_id
+        return self.model.model.infer(
+            x=x,
+            x_lengths=x_lengths,
+            sid=sid,
+            tone=tones,
+            language=lang_id,
+            bert=bert,
+            ja_bert=ja_bert,
+            noise_scale=noise_scale,
+            noise_scale_w=noise_scale_w,
+            length_scale=length_scale,
+        )[0]
+
+
+def main():
+    generate_lexicon()
+
+    language = "EN"
+    model = TTS(language=language, device="cpu")
+
+    generate_tokens(model.hps["symbols"])
+
+    torch_model = ModelWrapper(model)
+
+    opset_version = 13
+    x = torch.randint(low=0, high=10, size=(60,), dtype=torch.int64)
+    print(x.shape)
+    x_lengths = torch.tensor([x.size(0)], dtype=torch.int64)
+    sid = torch.tensor([1], dtype=torch.int64)
+    tones = torch.zeros_like(x)
+
+    noise_scale = torch.tensor([1.0], dtype=torch.float32)
+    length_scale = torch.tensor([1.0], dtype=torch.float32)
+    noise_scale_w = torch.tensor([1.0], dtype=torch.float32)
+
+    x = x.unsqueeze(0)
+    tones = tones.unsqueeze(0)
+
+    filename = "model.onnx"
+
+    torch.onnx.export(
+        torch_model,
+        (
+            x,
+            x_lengths,
+            tones,
+            sid,
+            noise_scale,
+            length_scale,
+            noise_scale_w,
+        ),
+        filename,
+        opset_version=opset_version,
+        input_names=[
+            "x",
+            "x_lengths",
+            "tones",
+            "sid",
+            "noise_scale",
+            "length_scale",
+            "noise_scale_w",
+        ],
+        output_names=["y"],
+        dynamic_axes={
+            "x": {0: "N", 1: "L"},
+            "x_lengths": {0: "N"},
+            "tones": {0: "N", 1: "L"},
+            "y": {0: "N", 1: "S", 2: "T"},
+        },
+    )
+
+    meta_data = {
+        "model_type": "melo-vits",
+        "comment": "melo",
+        "version": 2,
+        "language": "English",
+        "add_blank": int(model.hps.data.add_blank),
+        "n_speakers": len(model.hps.data.spk2id),  # 5
+        "jieba": 0,
+        "sample_rate": model.hps.data.sampling_rate,
+        "bert_dim": 1024,
+        "ja_bert_dim": 768,
+        "speaker_id": 0,
+        "lang_id": language_id_map[model.language],
+        "tone_start": language_tone_start_map[model.language],
+        "url": "https://github.com/myshell-ai/MeloTTS",
+        "license": "MIT license",
+        "description": "MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai",
+    }
+    add_meta_data(filename, meta_data)
+
+
+if __name__ == "__main__":
+    main()

 #!/usr/bin/env python3
+# This script export ZH_EN TTS model, which supports both Chinese and English.
+# This model has only 1 speaker.
+
 from typing import Any, Dict
 
 import onnx

@@ -38,4 +38,24 @@ tail tokens.txt
 
 ./test.py
 
+mkdir zh_en
+mv -v *.onnx zh_en/
+mv -v lexicon.txt zh_en
+mv -v tokens.txt zh_en
+cp -v README.md zh_en
+
+ls -lh
+echo "---"
+ls -lh zh_en
+
+./export-onnx-en.py
+
+mkdir en
+mv -v *.onnx en/
+mv -v lexicon.txt en
+mv -v tokens.txt en
+cp -v README.md en
+
+ls -lh en
+
 ls -lh

@@ -152,10 +152,6 @@
 #define SHERPA_ONNX_READ_META_DATA_STR_ALLOW_EMPTY(dst, src_key)           \
   do {                                                                     \
     auto value = LookupCustomModelMetaData(meta_data, src_key, allocator); \
-    if (value.empty()) {                                                   \
-      SHERPA_ONNX_LOGE("'%s' does not exist in the metadata", src_key);    \
-      exit(-1);                                                            \
-    }                                                                      \
                                                                            \
     dst = std::move(value);                                                \
   } while (0)

@@ -48,6 +48,20 @@ class MeloTtsLexicon::Impl {
     }
   }
 
+  Impl(const std::string &lexicon, const std::string &tokens,
+       const OfflineTtsVitsModelMetaData &meta_data, bool debug)
+      : meta_data_(meta_data), debug_(debug) {
+    {
+      std::ifstream is(tokens);
+      InitTokens(is);
+    }
+
+    {
+      std::ifstream is(lexicon);
+      InitLexicon(is);
+    }
+  }
+
   std::vector<TokenIDs> ConvertTextToTokenIds(const std::string &_text) const {
     std::string text = ToLowerCase(_text);
     // see
@@ -65,21 +79,39 @@ class MeloTtsLexicon::Impl {
     s = std::regex_replace(s, punct_re4, "!");
 
     std::vector<std::string> words;
-    bool is_hmm = true;
-    jieba_->Cut(text, words, is_hmm);
-
-    if (debug_) {
-      SHERPA_ONNX_LOGE("input text: %s", text.c_str());
-      SHERPA_ONNX_LOGE("after replacing punctuations: %s", s.c_str());
-
-      std::ostringstream os;
-      std::string sep = "";
-      for (const auto &w : words) {
-        os << sep << w;
-        sep = "_";
-      }
+    if (jieba_) {
+      bool is_hmm = true;
+      jieba_->Cut(text, words, is_hmm);
+
+      if (debug_) {
+        SHERPA_ONNX_LOGE("input text: %s", text.c_str());
+        SHERPA_ONNX_LOGE("after replacing punctuations: %s", s.c_str());
+
+        std::ostringstream os;
+        std::string sep = "";
+        for (const auto &w : words) {
+          os << sep << w;
+          sep = "_";
+        }
 
-      SHERPA_ONNX_LOGE("after jieba processing: %s", os.str().c_str());
+        SHERPA_ONNX_LOGE("after jieba processing: %s", os.str().c_str());
+      }
+    } else {
+      words = SplitUtf8(text);
+
+      if (debug_) {
+        fprintf(stderr, "Input text in string (lowercase): %s\n", text.c_str());
+        fprintf(stderr, "Input text in bytes (lowercase):");
+        for (uint8_t c : text) {
+          fprintf(stderr, " %02x", c);
+        }
+        fprintf(stderr, "\n");
+        fprintf(stderr, "After splitting to words:");
+        for (const auto &w : words) {
+          fprintf(stderr, " %s", w.c_str());
+        }
+        fprintf(stderr, "\n");
+      }
     }
 
     std::vector<TokenIDs> ans;
@@ -241,6 +273,7 @@ class MeloTtsLexicon::Impl {
           {std::move(word), TokenIDs{std::move(ids64), std::move(tone_list)}});
     }
 
+    // For Chinese+English MeloTTS
     word2ids_["呣"] = word2ids_["母"];
     word2ids_["嗯"] = word2ids_["恩"];
   }
@@ -268,6 +301,12 @@ MeloTtsLexicon::MeloTtsLexicon(const std::string &lexicon,
     : impl_(std::make_unique<Impl>(lexicon, tokens, dict_dir, meta_data,
                                    debug)) {}
 
+MeloTtsLexicon::MeloTtsLexicon(const std::string &lexicon,
+                               const std::string &tokens,
+                               const OfflineTtsVitsModelMetaData &meta_data,
+                               bool debug)
+    : impl_(std::make_unique<Impl>(lexicon, tokens, meta_data, debug)) {}
+
 std::vector<TokenIDs> MeloTtsLexicon::ConvertTextToTokenIds(
     const std::string &text, const std::string & /*unused_voice = ""*/) const {
   return impl_->ConvertTextToTokenIds(text);

@@ -22,6 +22,9 @@ class MeloTtsLexicon : public OfflineTtsFrontend {
                  const std::string &dict_dir,
                  const OfflineTtsVitsModelMetaData &meta_data, bool debug);
 
+  MeloTtsLexicon(const std::string &lexicon, const std::string &tokens,
+                 const OfflineTtsVitsModelMetaData &meta_data, bool debug);
+
   std::vector<TokenIDs> ConvertTextToTokenIds(
       const std::string &text,
       const std::string &unused_voice = "") const override;

@@ -349,6 +349,10 @@ class OfflineTtsVitsImpl : public OfflineTtsImpl {
           config_.model.vits.lexicon, config_.model.vits.tokens,
           config_.model.vits.dict_dir, model_->GetMetaData(),
           config_.model.debug);
+    } else if (meta_data.is_melo_tts && meta_data.language == "English") {
+      frontend_ = std::make_unique<MeloTtsLexicon>(
+          config_.model.vits.lexicon, config_.model.vits.tokens,
+          model_->GetMetaData(), config_.model.debug);
     } else if (meta_data.jieba && !config_.model.vits.dict_dir.empty()) {
       frontend_ = std::make_unique<JiebaLexicon>(
           config_.model.vits.lexicon, config_.model.vits.tokens,

@@ -46,8 +46,10 @@ class OfflineTtsVitsModel::Impl {
   }
 
   Ort::Value Run(Ort::Value x, Ort::Value tones, int64_t sid, float speed) {
-    // For MeloTTS, we hardcode sid to the one contained in the meta data
-    sid = meta_data_.speaker_id;
+    if (meta_data_.num_speakers == 1) {
+      // For MeloTTS, we hardcode sid to the one contained in the meta data
+      sid = meta_data_.speaker_id;
+    }
 
     auto memory_info =
         Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);

@@ -408,10 +408,10 @@ std::string LookupCustomModelMetaData(const Ort::ModelMetadata &meta_data,
 // For other versions, we may need to change it
 #if ORT_API_VERSION >= 12
   auto v = meta_data.LookupCustomMetadataMapAllocated(key, allocator);
-  return v.get();
+  return v ? v.get() : "";
 #else
   auto v = meta_data.LookupCustomMetadataMap(key, allocator);
-  std::string ans = v;
+  std::string ans = v ? v : "";
   allocator->Free(allocator, v);
   return ans;
 #endif