
Senior Voice Synthesis Engineer
3 days ago
We are seeking a skilled AI developer with a strong background in building and optimizing voice models for speech synthesis, voice cloning, speech recognition, and/or voice transformation.
The ideal candidate will have hands-on experience in developing, training, and refining AI models for voice synthesis. This includes:
- Developing and optimizing text-to-speech models that achieve human-like voice synthesis, maintaining the unique style of voice actors across multiple languages.
- Implementing real-time processing solutions that reduce inference time to under 1 second, enhancing user interaction and experience.
- Managing large-scale datasets for voice cloning projects, ensuring high performance and reliability while supporting multilingual transcriptions.
Key Responsibilities include:
Key Responsibilities- Designing, developing, and fine-tuning deep learning models for voice synthesis (e.g., TTS, voice cloning).
- Implementing and optimizing neural network architectures such as Tacotron, FastSpeech, WaveNet, or similar.
- Collecting, preprocessing, and augmenting speech datasets.
- Collaborating with product and engineering teams to integrate voice models into production systems.
- Evaluating and ensuring quality assurance of voice model outputs.
- Researching and staying current on advancements in speech processing, audio generation, and machine learning.
Requirements include:
Requirements- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
- Strong experience with Python and machine learning libraries (e.g., PyTorch, TensorFlow).
- Hands-on experience with speech/audio processing and relevant toolkits (e.g., Librosa, ESPnet, Kaldi).
- Familiarity with voice model architectures (TTS, ASR, vocoders).
- Understanding of deep learning concepts and model training processes.
Preferred qualifications include:
Preferred Qualifications- Experience with deploying models to real-time applications or mobile devices.
- Knowledge of data labeling, voice dataset creation, and noise handling techniques.
- Experience with cloud-based AI/ML infrastructure (e.g., AWS, GCP).
- Contributions to open-source projects or published papers in speech/voice-related domains.
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