PSST! Prosodic Speech Segmentation With Transformers

[Paper] [Colab Tutorial]

New: [Quantized Tutorial]

Easy to use, prosodically-informed text-to-speech!

• Integrated with intonation unit ~ intonational phrase ~ prosodic unit
• Boundaries are transcribed with the !!!!! token.
• Finetuned from Whisper (medium.en)

Generate transcriptions using PSST:

!pip install transformers librosa

Next, import the necessary libraries and functions from the installed modules.

from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import librosa

Define a function init_model_processor to initialize the model and processor, which will be used to generate transcriptions from audio inputs.

def init_model_processor(pretrained_name="NathanRoll/psst-medium-en", gpu=False):
    """Initializes the model and processor with the pre-trained weights.
    
    Returns:
      model, processor
    """
    processor = AutoProcessor.from_pretrained(pretrained_name)
    device = "cuda:0" if gpu else "cpu"
    model = AutoModelForSpeechSeq2Seq.from_pretrained(pretrained_name).to(device)
    
    return model, processor

The function generate_transcription utilizes the initialized model and processor to create a textual transcription of provided audio data.

def generate_transcription(audio, model, processor, gpu=False):
    """Generate a transcription from audio using a pre-trained model."""
    inputs = processor(audio, return_tensors="pt", sampling_rate=16000)
    input_features = inputs.input_features.to("cuda:0") if gpu else inputs.input_features

    generated_ids = model.generate(input_features, max_length=250)

    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0].replace('!!!!!', '|')

Next, use librosa to load and resample the audio file.

y, sr = librosa.load('gettysburg.wav') # Your audio file here
audio = librosa.resample(y, orig_sr=sr, target_sr=16000)

Finally, initialize the model and processor, then generate and display the transcription of the resampled audio.

# Initialize model and processor
model, processor = init_model_processor(gpu=False)

# Generate Transcription
transcript = generate_transcription(audio, model, processor, gpu=False)
print(transcript)

Output:

Four score and seven years ago <|IU_Boundary|> our fathers brought forth on this continent <|IU_Boundary|> a new nation <|IU_Boundary|> conceived in liberty <|IU_Boundary|> and dedicated to the proposition <|IU_Boundary|> that all men are created equal <|IU_Boundary|> Now we are engaged in a great civil war <|IU_Boundary|> testing whether that nation <|IU_Boundary|> or any nation so conceived and so dedicated <|IU_Boundary|> can long endure

You may cite this work as:

@inproceedings{roll-etal-2023-psst,
    title = "{PSST}! Prosodic Speech Segmentation with Transformers",
    author = "Roll, Nathan  and
      Graham, Calbert  and
      Todd, Simon",
    editor = "Jiang, Jing  and
      Reitter, David  and
      Deng, Shumin",
    booktitle = "Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.conll-1.31",
    pages = "476--487",
}