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* Fix bug in SpeechT5 speech decoder prenet's forward method - Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues. - Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact. - This change resolves a critical bug affecting the model's performance in handling speaker embeddings. * Refactor SpeechT5 text to speech integration tests - Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite. - Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations. - Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing. - Fixed existing test cases where incorrect assumptions about output shapes led to potential errors. * Fix bug in SpeechT5 speech decoder prenet's forward method - Removed redundant `repeat` operation on speaker_embeddings in the forward method. This line was erroneously duplicating the embeddings, leading to incorrect input size for concatenation and performance issues. - Maintained original functionality of the method, ensuring the integrity of the speech decoder prenet's forward pass remains intact. - This change resolves a critical bug affecting the model's performance in handling speaker embeddings. * Refactor SpeechT5 text to speech integration tests - Updated SpeechT5ForTextToSpeechIntegrationTests to accommodate the variability in sequence lengths due to dropout in the speech decoder pre-net. This change ensures that our tests are robust against random variations in generated speech, enhancing the reliability of our test suite. - Removed hardcoded dimensions in test assertions. Replaced with dynamic checks based on model configuration and seed settings, ensuring tests remain valid across different runs and configurations. - Added new test cases to thoroughly validate the shapes of generated spectrograms and waveforms. These tests leverage seed settings to ensure consistent and predictable behavior in testing, addressing potential issues in speech generation and vocoder processing. - Fixed existing test cases where incorrect assumptions about output shapes led to potential errors. * Enhance handling of speaker embeddings in SpeechT5 - Refined the generate and generate_speech functions in the SpeechT5 class to robustly handle two scenarios for speaker embeddings: matching the batch size (one embedding per sample) and one-to-many (a single embedding for all samples in the batch). - The update includes logic to repeat the speaker embedding when a single embedding is provided for multiple samples, and a ValueError is raised for any mismatched dimensions. - Also added corresponding test cases to validate both scenarios, ensuring complete coverage and functionality for diverse speaker embedding situations. * Improve Test Robustness with Randomized Speaker Embeddings
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