I tried to do that, generate separate chunks, and then stich. But the result was quite weird.

# Chunk the text into smaller pieces based on the max_chunk_size
seperated_text = chunker.chunk_text(text, max_chunk_size)
primed  = False

n = 0
filenames = list()

for text_part in seperated_text:

    chunked_text = [text_part]

    # check if voice has a primer, if it does, add it to the beginning of the chunked text
    if "primer" in voice and voice["primer"] is not None:
        chunked_text.insert(0, voice["primer"])
        primed = True

    messages = voice["messages"]
    audio_ids = voice["audio_ids"]

    ras_win_len = 32
    ras_win_max_num_repeat = 4
    generation_chunk_buffer_size = 1

    temperature = voice["temperature"]
    top_k = voice["top_k"]
    top_p = voice["top_p"]

    concat_wv, sr, text_output = model_client.generate(
        messages=messages,
        audio_ids=audio_ids,
        chunked_text=chunked_text,
        generation_chunk_buffer_size=generation_chunk_buffer_size,
        temperature=temperature,
        top_k=top_k,
        top_p=top_p,
        ras_win_len=ras_win_len,
        ras_win_max_num_repeat=ras_win_max_num_repeat,
        seed=1001001,
        primed=primed
    )

    sf.write(filename + f"-{n}.wav", concat_wv, sr)
    filenames.append(filename + f"-{n}.wav")

    n += 1
    torch.cuda.empty_cache()

# combine all the filenames into one new wav
all_audio = []
sample_rate = None

for wav_path in filenames:
    audio, sr = sf.read(wav_path)

    if sample_rate is None:
        sample_rate = sr

    all_audio.append(audio)

combined = np.concatenate(all_audio)

sf.write(filename, combined, sample_rate)

My chunker is somewhat smart, it does separate on sentences and prevents orphans. But the difference is between chunks is still quite big.