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You actually dont need to have phone level alignment for your data. Both hybrid and end-2-end approaches can work with utterance level alignment. For the hybrid approach, you would need a lexicon which maps each unique word in your training transcription to its phone sequence. You can obtain this with CMU's tool. For end-2-end approach you will need a byte pair encoder to tokenize the words in the transcriptions to its sub-words.
This is relatively small amount of speech to train the model from scratch, but you can train using another pre-trained model for initialization. There are numbers of end-to-end ASR toolkits which can be used for this: https://github.com/NVIDIA/NeMo and https://github.com/espnet/espnet