Very slow motion speech for some combinations
@computersrmyfriends  Have you been able to get vocoder to create more than 11 seconds of audio? I am unable to figure out how. This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days. This issue was closed because it has been inactive for 7 days since being marked as stale.
```
def text_to_speech(text, tts_model_name, vocoder_model_name, max_length=100, file_name_prefix=""):
    torch.set_grad_enabled(False)

    sampling_rates = {
        "tts_en_tacotron2": 22050,
        "tts_en_fastpitch": 22050,
        "tts_en_fastpitch_ipa": 22050,
        "tts_en_fastpitch_multispeaker": 44100,
        "tts_de_fastpitch_singleSpeaker_thorstenNeutral_2102": 22050,
        "tts_de_fastpitch_singleSpeaker_thorstenNeutral_2210": 22050,
        "tts_de_fastpitch_multispeaker_5": 44100,
        "tts_es_fastpitch_multispeaker": 44100,
        "tts_zh_fastpitch_sfspeech": 22050,
        "tts_en_waveglow_88m": 22050,
        "tts_en_hifigan": 22050,
        "tts_en_lj_hifigan_ft_mixertts": 22050,
        "tts_en_lj_hifigan_ft_mixerttsx": 22050,
        "tts_en_hifitts_hifigan_ft_fastpitch": 44100,
        "tts_de_hifigan_singleSpeaker_thorstenNeutral_2102": 22050,
        "tts_de_hifigan_singleSpeaker_thorstenNeutral_2210": 22050,
        "tts_de_hui_hifigan_ft_fastpitch_multispeaker_5": 44100,
        "tts_es_hifigan_ft_fastpitch_multispeaker": 44100,
        "tts_zh_hifigan_sfspeech": 22050
    }

    # Load TTS model
    if "tacotron" in tts_model_name.lower():
        tts_model = nemo_tts.models.Tacotron2Model.from_pretrained(model_name=tts_model_name)
    elif "fastpitch" in tts_model_name.lower():
        tts_model = nemo_tts.models.FastPitchModel.from_pretrained(model_name=tts_model_name)
    else:
        raise ValueError("Unsupported TTS model name")

    # Load vocoder model
    if "waveglow" in vocoder_model_name.lower():
        vocoder = nemo_tts.models.WaveGlowModel.from_pretrained(model_name=vocoder_model_name)
    elif "hifigan" in vocoder_model_name.lower():
        vocoder = nemo_tts.models.HifiGanModel.from_pretrained(model_name=vocoder_model_name)
    else:
        raise ValueError("Unsupported vocoder model name")

    tts_model.eval()
    vocoder.eval()

    # Split text into chunks
    chunks = split_text(text, max_length)
    full_audio = []

    for chunk in chunks:
        parsed = tts_model.parse(chunk)
        spectrogram = tts_model.generate_spectrogram(tokens=parsed)
        audio = vocoder.convert_spectrogram_to_audio(spec=spectrogram)
        audio_numpy = audio.to().numpy().T
        full_audio.extend(audio_numpy)

    full_audio_np = np.hstack(full_audio)
    file_name = f'samples1/{file_name_prefix}_{tts_model_name}_{vocoder_model_name}.wav'

# Determine the correct sampling rate
    tts_samplerate = sampling_rates.get(tts_model_name.split('_')[1], 22050)  # Get default based on TTS model type
    vocoder_samplerate = sampling_rates.get(vocoder_model_name.split('_')[1], 22050)  # Get default based on vocoder model type
    samplerate = max(tts_samplerate, vocoder_samplerate)  # Choose the higher rate if they differ
    sf.write(file_name, full_audio_np, samplerate)
    return file_name

# List all TTS and vocoder models
tts_models = [model.pretrained_model_name for model in nemo_tts.models.Tacotron2Model.list_available_models()] + \
             [model.pretrained_model_name for model in nemo_tts.models.FastPitchModel.list_available_models()]

vocoder_models = [model.pretrained_model_name for model in nemo_tts.models.WaveGlowModel.list_available_models()] + \
                  [model.pretrained_model_name for model in nemo_tts.models.HifiGanModel.list_available_models()]
```




Here's what I do for splitting since long texts dont work out afaik,

```
def split_text(text, max_length):
    words = text.split()
    chunks = []
    current_chunk = ""

    for word in words:
        # Check if adding the word would exceed the max length
        if len(current_chunk) + len(word) + 1 > max_length:
            # If the chunk is too long and doesn't end with punctuation, add it as is
            if not current_chunk[-1] in ".!?":
                chunks.append(current_chunk)
                current_chunk = word
            else:
                # If it ends with punctuation, find the nearest punctuation to split
                nearest_punct = max(current_chunk.rfind("."), current_chunk.rfind("!"), current_chunk.rfind("?"))
                split_at = nearest_punct + 1
                chunks.append(current_chunk[:split_at])
                current_chunk = current_chunk[split_at:].strip() + " " + word
        else:
            current_chunk = current_chunk + " " + word if current_chunk else word

    if current_chunk:
        chunks.append(current_chunk)

    return chunks
```


And then, When I use this code to test different model combinations like this,

```
# Generate speech for each combination of TTS and vocoder models
for tts_model_name in tts_models:
    for vocoder_model_name in vocoder_models:
        print(f"Generating speech with TTS model: {tts_model_name} and vocoder model: {vocoder_model_name}")
        text_to_speech(longtext, tts_model_name, vocoder_model_name, max_length=100, file_name_prefix="speech_output")
```



Among the resultant files, many are in slow motion. like these combinations:

_tts_en_tacotron2_tts_de_hui_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_tacotron2_tts_es_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_fastpitch_ipa_tts_es_hifigan_ft_fastpitch_multispeaker.wav
speech_output__tts_en_fastpitch_ipa_tts_en_hifitts_hifigan_ft_fastpitch.wav

Multispeaker always results in extreme slow motion low pitch voices. As you can see, I have tried setting the frequencies too based on my limited knowledge.

Any thoughts?