MSP Processor with KenLM 3-gram Decoder
MSP-Processor-With-LM adds beam-search decoding to the MSP model family. It combines the MSP tokenizer, pyctcdecode, and an English KenLM 3-gram language model; it does not generate logits and is not a standalone speech-recognition model.
The language model was built exclusively from transcripts in the train and pretrain splits of MahmoodAnaam/LRS2-Text. Validation and test transcripts were not used to build it.
The processor is compatible with CTC logits from:
Usage
Install the decoder dependencies:
python -m pip install pyctcdecode
python -m pip install "https://github.com/kpu/kenlm/archive/master.zip"
Decode existing logits
from transformers import AutoProcessor
lm_processor = AutoProcessor.from_pretrained(
"MahmoodAnaam/MSP-Processor-With-LM",
trust_remote_code=True,
)
# logits: NumPy array with shape (batch, time, vocabulary)
result = lm_processor.batch_decode(logits)
print(result.text[0])
End-to-end MSP-AVSR example
import torch
from transformers import AutoModelForCTC, AutoProcessor
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "MahmoodAnaam/MSP-AVSR"
model_processor = AutoProcessor.from_pretrained(
model_id,
trust_remote_code=True,
)
lm_processor = AutoProcessor.from_pretrained(
"MahmoodAnaam/MSP-Processor-With-LM",
trust_remote_code=True,
)
model = AutoModelForCTC.from_pretrained(
model_id,
trust_remote_code=True,
).eval().to(device)
inputs = model_processor(
audio="sample.mp4",
videos="sample.mp4",
return_tensors="pt",
).to(device)
# OR
inputs = lm_processor(
audio="sample.mp4",
videos="sample.mp4",
return_tensors="pt",
).to(device)
with torch.inference_mode():
logits = model(**inputs).logits
greedy_text = model_processor.tokenizer.batch_decode(
logits.argmax(dim=-1)
)[0]
beam_text = lm_processor.batch_decode(
logits.cpu().detach().numpy()
).text[0]
print("Greedy:", greedy_text)
print("Beam search:", beam_text)
Use the corresponding model processor to prepare audio or video inputs, and use this LM processor to decode the resulting logits. For MSP-ASR or MSP-VSR, change model_id and provide only the supported input modality.
batch_decode supports controls such as beam_width, beam_prune_logp, token_min_logp, hotwords, hotword_weight, alpha, beta, n_best, and output_word_offsets. Tune these values on a validation set for the target domain; increasing language-model weight does not guarantee a lower WER.