mirror of
https://github.com/k2-fsa/sherpa-onnx.git
synced 2026-11-10 21:11:03 +08:00
Fangjun Kuang
0b3647632f
Fixes #3731 espeak-ng and piper-phonemize are no longer built into sherpa-onnx. Text frontends are now: - **lexicon** — built in, no extra dependency - **phonemize** — phonemize outside sherpa-onnx (e.g. [piper-phonemize](https://github.com/csukuangfj/piper-phonemize)) and pass the result in. That library is GPL, so it stays optional: sherpa-onnx is GPL-free after this PR. ## API - `SherpaOnnxGenerationConfig` gains `phoneme_codepoints` and `tokens` (mutually exclusive alternatives to `text`) - `data_dir` on the TTS model configs is deprecated and ignored; the ABI slot is kept - all bindings updated: C/C++, Python, Java, Kotlin, Go, Rust, C#, Swift, Pascal, Dart/Flutter, node-addon, WASM, HarmonyOS
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Test ZipVoice Chinese+English TTS model using lexicon
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(no espeak-ng data needed).
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If you don't want to use a lexicon, please see
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test-offline-tts-zipvoice-phonemize.py which uses piper_phonemize instead.
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Passes two lexicon files (comma-separated): the zh lexicon from the model
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directory and the en lexicon (lexicon-en-us.txt).
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Supported extra parameters in gen_config.extra (all optional):
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- min_words_in_sentence, int, default 5.
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Merge adjacent sentences if the number of words is less than this.
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Each CJK character counts as one word; English words are space-separated.
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- max_words_in_sentence, int, default 20.
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Split a sentence into chunks if the number of words exceeds this.
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Splits at punctuation or space boundaries.
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- num_steps, int, default 4.
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Number of flow-matching denoising steps.
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- debug, int, default from model config.
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Download model, vocoder, and lexicon:
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/sherpa-onnx-zipvoice-distill-int8-zh-en-emilia.tar.bz2
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tar xf sherpa-onnx-zipvoice-distill-int8-zh-en-emilia.tar.bz2
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/vocoder-models/vocos_24khz.onnx
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/lexicon-en-us.txt
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"""
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import time
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import numpy as np
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import sherpa_onnx
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import soundfile as sf
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model_dir = "./sherpa-onnx-zipvoice-distill-int8-zh-en-emilia"
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tts_config = sherpa_onnx.OfflineTtsConfig(
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model=sherpa_onnx.OfflineTtsModelConfig(
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zipvoice=sherpa_onnx.OfflineTtsZipvoiceModelConfig(
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tokens=f"{model_dir}/tokens.txt",
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encoder=f"{model_dir}/encoder.int8.onnx",
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decoder=f"{model_dir}/decoder.int8.onnx",
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vocoder="./vocos_24khz.onnx",
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lexicon=f"{model_dir}/lexicon.txt,./lexicon-en-us.txt",
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),
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debug=True,
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),
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)
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tts = sherpa_onnx.OfflineTts(tts_config)
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# Load reference audio
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ref_wav = f"{model_dir}/test_wavs/news-female.wav"
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ref_audio, ref_sr = sf.read(ref_wav)
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ref_audio = ref_audio.astype(np.float32)
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text = "在这个快速发展的时代，人工智能artificial intelligence技术正在改变我们的生活方式。语音合成speech synthesis作为重要应用之一，让机器能够用自然流畅的语音与人类进行交流。"
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gen_config = sherpa_onnx.GenerationConfig()
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gen_config.sid = 0
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gen_config.speed = 1.0
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gen_config.reference_audio = ref_audio.tolist()
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gen_config.reference_sample_rate = ref_sr
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gen_config.reference_text = "各位村民, 大家新年好! 近期, 湖北省武汉市等多个地区"
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start = time.time()
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audio = tts.generate(text, gen_config)
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end = time.time()
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assert len(audio.samples) > 0, "No audio generated!"
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assert audio.sample_rate > 0, "Invalid sample rate!"
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filename = "test-zipvoice-lexicon.wav"
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sf.write(filename, audio.samples, samplerate=audio.sample_rate)
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elapsed_seconds = end - start
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audio_duration = len(audio.samples) / audio.sample_rate
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rtf = elapsed_seconds / audio_duration
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print(f"Saved to {filename}")
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print(f"Elapsed seconds: {elapsed_seconds:.3f}")
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print(f"Audio duration in seconds: {audio_duration:.3f}")
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print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {rtf:.3f}") |