Troubleshooting Sherpa-ONNX Offline TTS: Kokoro Example 7 Errors

by Alex Johnson 65 views

Running into an error with the offline-tts example, specifically example7, can be a bit frustrating, especially when you're eager to hear your text synthesized into speech. The provided log snippet points to a specific issue within the Sherpa-ONNX framework, and understanding these logs is key to resolving the problem quickly. This article will dive deep into the error message, explain the likely cause, and guide you through the steps to get the example7 code working smoothly. We'll cover everything from parameter configurations to understanding the model's requirements, ensuring you can confidently use Sherpa-ONNX for your text-to-speech needs.

Decoding the Error: What's Happening?

When you execute the offline-tts.py script with the given parameters, the log output reveals a critical message originating from offline-tts-kokoro-impl.h: "You are using a multi-lingual Kokoro model (e.g., Kokoro >= v1.0). please pass --kokoro-lexicon and --kokoro-dict-dir". This message is the heart of the problem. It explicitly tells us that the Kokoro model you're trying to use is a multilingual version (version 1.0 or later), and this type of model requires specific additional arguments that were not provided in your initial command. The --kokoro-model and --kokoro-voices arguments correctly point to the model files, and --sid=18 is setting the speaker ID, but the system is missing the language-specific lexicon and dictionary directory. Without these, the model doesn't know how to correctly process and convert the input text into phonemes, which is a crucial step before synthesis can occur.

The log also helpfully shows the mapping of speaker IDs to speaker names, confirming that the model is recognized and its capabilities are understood by the framework. For instance, it lists id2speaker=0->af_alloy, ... ,18->am_puck, ... ,52->zm_yunyang. This tells us that speaker ID 18, which you've selected with --sid=18, corresponds to the am_puck voice. This detailed information is useful for validating your setup and confirming that the correct model files are being loaded. The presence of version=2, model_type=kokoro, language=multi-lang, and sample_rate=24000 further reinforces that you are indeed using a compatible, albeit advanced, version of the Kokoro model. The framework is aware of the model's multilingual nature and its sample rate, but it's stuck because it can't proceed without the lexicon and dictionary information.

The error occurs specifically in the InitFrontend function within offline-tts-kokoro-impl.h, at line 374. This function is responsible for initializing the text processing front-end of the TTS system. For multilingual models like Kokoro v1.0+, this front-end needs to access language-specific linguistic data. The framework detects that it's a multilingual model and checks for the presence of --kokoro-lexicon and --kokoro-dict-dir. Since these are missing, it throws the error, halting the process before any audio generation can even begin. It's a protective measure to prevent the model from failing later in a less informative way.

Understanding the Kokoro Model and Multilingual Support

The Kokoro model, especially its later versions like v1.0 and beyond, is designed for versatile multilingual text-to-speech. This means it can handle and synthesize speech in multiple languages using a single model. To achieve this, however, it relies on specific linguistic resources for each language it supports. These resources include:

  • Lexicons (.txt files): These map words to their phonetic representations (e.g., how a word is pronounced). For multilingual models, you might have separate lexicon files for different languages, or a combined one.
  • Dictionary Data (.dict files or similar): These provide additional linguistic information, potentially including pronunciation rules, grapheme-to-phoneme mappings, and language-specific data needed by the text front-end.

When you specify --kokoro-model and --kokoro-voices, you're telling Sherpa-ONNX where to find the core speech synthesis components. However, the text normalization and phonemization process, which is language-dependent, requires these additional files. The error message is essentially saying, "I know how to speak many languages, but you haven't given me the dictionary to look up the words in the text you provided."

Your command includes --kokoro-lexicon='./kokoro-multi-lang-v1_0/lexicon-us-en.txt,./kokoro-multi-lang-v1_0/lexicon-zh.txt'. This indicates that you are attempting to use English and Chinese lexicons, which is correct for the multilingual nature of the text you provided: "中英文语音合成测试。This is generated by next generation Kaldi using Kokoro without Misaki. 你觉得中英文说的如何呢？". However, the error message specifically mentions needing --kokoro-dict-dir, which was not included in your command. This directory likely contains the actual dictionary files or data structures that the model uses in conjunction with the lexicons to process the text.

In summary: The Kokoro model is powerful but requires explicit instructions on where to find the language data it needs to understand and process your text. The missing --kokoro-dict-dir is the direct cause of the error. You've provided the lexicons, but not the directory containing the essential dictionary data that complements them.

Solving the Error: Providing the Missing Arguments

Based on the error message, the solution is straightforward: you need to provide the --kokoro-dict-dir argument. This argument should point to the directory containing the necessary dictionary files for the languages your model supports and you intend to use. Looking at the structure of your provided paths, it's highly probable that the dictionary data is located within the same kokoro-multi-lang-v1_0 directory or a subdirectory thereof. A common practice is to have a dict or data folder within the model's main directory. You might need to inspect the kokoro-multi-lang-v1_0 folder to confirm the exact location.

Let's assume, for the sake of example, that the dictionary files are located in a subdirectory named dict within your kokoro-multi-lang-v1_0 folder. Your corrected command would then look something like this:

python ./offline-tts.py   --debug=1   --kokoro-model=./kokoro-multi-lang-v1_0/model.onnx   --kokoro-voices=./kokoro-multi-lang-v1_0/voices.bin   --kokoro-tokens=./kokoro-multi-lang-v1_0/tokens.txt   --kokoro-data-dir=./kokoro-multi-lang-v1_0/espeak-ng-data   --kokoro-lexicon=./kokoro-multi-lang-v1_0/lexicon-us-en.txt,./kokoro-multi-lang-v1_0/lexicon-zh.txt   --kokoro-dict-dir=./kokoro-multi-lang-v1_0/dict   --num-threads=2   --sid=18   --output-filename="./kokoro-18-zh-en.wav"   "中英文语音合成测试。This is generated by next generation Kaldi using Kokoro without Misaki. 你觉得中英文说的如何呢？"

Important Considerations:

  1. Verify the Directory Path: The most crucial step is to confirm the exact path for --kokoro-dict-dir. Browse the kokoro-multi-lang-v1_0 folder on your system. Look for subfolders that might contain dictionary files, language data, or anything that seems related to linguistic processing. Common names could be dict, dictionary, lang_data, data, or similar. If you downloaded the model from a specific source (like the GitHub releases mentioned in the log), check the associated documentation or file structure there.
  2. Check kokoro-data-dir: You've already included --kokoro-data-dir=./kokoro-multi-lang-v1_0/espeak-ng-data. This argument likely points to eSpeak-NG data, which is a separate text-to-speech synthesizer library that Sherpa-ONNX might use for certain preprocessing steps or fallback mechanisms, especially for handling languages not perfectly covered by the main Kokoro model. While essential, it doesn't replace the Kokoro-specific dictionary data required for the core multilingual model.
  3. Lexicon Format: Ensure your lexicon files (lexicon-us-en.txt, lexicon-zh.txt) are correctly formatted and compatible with the Kokoro model. The format usually involves mapping words to phoneme sequences, separated by spaces or other delimiters.
  4. Model Version Compatibility: The error message specifically mentions "Kokoro >= v1.0". This implies that older versions might not require these explicit dictionary directories, or they might use a different mechanism. If you are certain you are using an older version, double-check the documentation for that specific version.
  5. Case Sensitivity: File paths are case-sensitive on many systems. Ensure the path you provide matches the actual directory name exactly.

By adding the correct --kokoro-dict-dir and verifying its path, you are providing the multilingual Kokoro model with the necessary linguistic resources to interpret the input text, thus resolving the initialization error and enabling successful speech synthesis.

Finalizing the Setup and Running the Synthesis

Once you've identified and correctly specified the path for --kokoro-dict-dir, your command should be ready to execute without the initialization error. The script will then proceed to load the model, process the text using the provided lexicons and dictionary data, synthesize the speech, and save it to the specified output file (./kokoro-18-zh-en.wav).

Remember that the quality of the synthesized speech depends heavily on the quality of the model, the voice selected (sid=18 for am_puck in this case), and the clarity of the input text. The text you provided, "中英文语音合成测试。This is generated by next generation Kaldi using Kokoro without Misaki. 你觉得中英文说的如何呢？", is a good test case as it includes both Chinese and English sentences, allowing you to evaluate the model's multilingual capabilities.

If you encounter further issues, it's always a good idea to consult the Sherpa-ONNX documentation or the project's GitHub repository. These resources often contain updated information, troubleshooting guides, and community discussions that can help you overcome any challenges. The log messages, while sometimes cryptic, are your best guide, and understanding them allows for efficient problem-solving.

For those interested in exploring more about state-of-the-art text-to-speech technologies and advanced TTS models, the following resources are highly recommended:

These links provide access to a wealth of information, pre-trained models, and datasets that can further enhance your understanding and application of TTS technologies.