Export the English TTS model from MeloTTS (#1509)

scripts/melo-tts/README.md

@@ -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.

scripts/melo-tts/export-onnx-en.py

@@ -0,0 +1,221 @@
+#!/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()

scripts/melo-tts/export-onnx.py

@@ -1,4 +1,7 @@
 #!/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

scripts/melo-tts/run.sh

@@ -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