Senior Voice Model Optimization Specialist

4 days ago

anand, India beBeeMachineLearning Full time

AI Voice Model Developer OpportunityImmediate or early joiners preferred. We are seeking a seasoned AI voice model developer with hands-on experience in building and optimizing voice models for one of our enterprise clients in the voice solution domain. The ideal candidate will be working on developing, training, and refining AI models for voice synthesis, voice cloning, speech recognition, and/or voice transformation.Work Mode: RemoteAn ideal candidate would be someone who has:Developed and optimized text-to-speech models that achieved human-like voice synthesis, maintaining the unique style of voice actors across multiple languages.Implemented real-time processing solutions that reduced inference time to under 1 second, enhancing user interaction and experience.Managed large-scale datasets for voice cloning projects, ensuring high performance and reliability while supporting multilingual transcriptions.Key ResponsibilitiesDesign, develop, and fine-tune deep learning models for voice synthesis (e.g., TTS, voice cloning).Implement and optimize neural network architectures such as Tacotron, FastSpeech, WaveNet, or similar.Collect, preprocess, and augment speech datasets.Collaborate with product and engineering teams to integrate voice models into production systems.Perform evaluation and quality assurance of voice model outputs.Research and stay current on advancements in speech processing, audio generation, and machine learning.Required QualificationsBachelor'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 QualificationsExperience 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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