Feature request: Update the pipeline for AudioLDM 2 so that 'transcript' can be consumed and text to speech created

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The current pipeline for AudioLDM 2 does not take in "transcript" field.
Hence, it does not create phonemes and hence does not allow for text-to-speech generation.

https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm2#diffusers.AudioLDM2Pipeline

Currently, only text-to-music and text-to-audio are supported. The latent difussion model is not guided for creating phonemes as in the original implementation with these two checkpoints:

- audioldm2-speech-ljspeech
- audioldm2-speech-gigaspeech

Here:
https://github.com/haoheliu/AudioLDM2/blob/main/audioldm2/pipeline.py#L78

and here:
https://github.com/haoheliu/AudioLDM2/blob/main/audioldm2/latent_diffusion/models/ddpm.py#L482

commandline from original repo:
`audioldm2 -t "A female reporter is speaking full of emotion" --transcription "Wish you have a good day"`

These two checkpoints naturally take phonemes into the batch so the checkpoints do consume "phoneme" as one of the fields in the batch natively.

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Add the "transcription" input param to allow to choose a TTS model from the two checkpoints above and hence allow for TTS task.

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Original repo implementation - is very slow and unoptimized.

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I believe the already implemented pipeline AudioLDM2 could be updated to take in the transcript field, update the batch, and load the additional two checkpoints trained on TTS task. However, I currently don't have enough knowledge to assess which part of the pipeline needs to be updated vs the original implementation in https://github.com/haoheliu/AudioLDM2/blob/main/audioldm2/latent_diffusion/models/ddpm.py#L482

Start with the existing AudioLDM2Pipeline and compare it with the linked original audioldm2/pipeline.py and latent_diffusion/models/ddpm.py implementations. Trace how the transcription input becomes phonemes and enters the batch, then identify how the audioldm2-speech-ljspeech and audioldm2-speech-gigaspeech checkpoints are selected. Done means the pipeline accepts transcription and supports text-to-speech generation with those checkpoints.

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