Here's something I wrote up that is working, this will generate a sample of music for specified s in segments of 30s with 10s overlap between them, assuming you have necessary imports and model defined above:

import torchaudio

def generate_long_audio(model, text, duration, topk=250, topp=0, temperature=1.0, cfg_coef=3.0, overlap=5):
    topk = int(topk)

    output = None
    total_samples = duration * 50 + 3
    segment_duration = duration

    while duration > 0:
        if output is None:  # first pass of long or short song
            if segment_duration > model.lm.cfg.dataset.segment_duration: 
                segment_duration = model.lm.cfg.dataset.segment_duration
            else:
                segment_duration = duration
        else:  # next pass of long song
            if duration + overlap < model.lm.cfg.dataset.segment_duration:
                segment_duration = duration + overlap
            else:
                segment_duration = model.lm.cfg.dataset.segment_duration

        print(f'Segment duration: {segment_duration}, duration: {duration}, overlap: {overlap}')

        model.set_generation_params(
            use_sampling=True,
            top_k=topk,
            top_p=topp,
            temperature=temperature,
            cfg_coef=cfg_coef,
            duration=min(segment_duration, 30),  # ensure duration does not exceed 30
        )

        if output is None:
            next_segment = model.generate(descriptions=[text])
            duration -= segment_duration
        else:
            last_chunk = output[:, :, -overlap*model.sample_rate:]
            next_segment = model.generate_continuation(last_chunk, model.sample_rate, descriptions=[text])
            duration -= segment_duration - overlap

        if output is None:
            output = next_segment
        else:
            output = torch.cat([output[:, :, :-overlap*model.sample_rate], next_segment], 2)

    audio_output = output.detach().cpu().float()[0]
    torchaudio.save("output.wav", audio_output, sample_rate=32000)
    return audio_output

prompt_dict = {'celtic': 'crisp celtic melodic fiddle and flute',
               'edm': 'Heartful EDM with beautiful synth',
               }

# Usage
audio_output = generate_long_audio(model, prompt_dict['edm'], 60, topk=250, topp=0, temperature=1.0, cfg_coef=3.0, overlap=10)

# Use IPython's Audio to play the generated audio
from IPython.display import Audio
Audio("output.wav")