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Running
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import difflib
import re
from functools import lru_cache
#import spaces
import gradio.components.audio as gr_audio
import torch
from transformers import pipeline
# ------------------- Utilities -------------------
def normalize_text(t: str, lower: bool = True) -> str:
"""For normalizing LLM-generated and human-generated strings.
For LLMs, this removes extraneous quote marks and spaces."""
# English-only normalization: lowercase, keep letters/digits/' and -
if lower:
t = t.lower()
# TODO: Previously was re.sub(r"[^a-z0-9'\-]+", " ", t); discuss normalizing for LLMs too.
t = re.sub(r"[^a-zA-Z0-9'\-.,]+", " ", t)
t = re.sub(r"\s+", " ", t).strip()
return t
#@spaces.GPU
@lru_cache(maxsize=2)
def get_asr_pipeline(model_id: str, device_preference: str) -> pipeline:
"""Cache an ASR pipeline.
Parameters:
model_id: String of desired ASR model.
device_preference: String of desired device for ASR processing, "cuda", "cpu", or "auto".
Returns:
transformers.pipeline ASR component.
"""
if device_preference == "cuda" and torch.cuda.is_available():
device = 0
elif device_preference == "auto":
device = 0 if torch.cuda.is_available() else -1
else:
device = -1
return pipeline(
"automatic-speech-recognition",
model=model_id, # use English-only Whisper models (.en)
device=device,
chunk_length_s=30,
return_timestamps=False,
)
def run_asr(audio_path: gr_audio, model_id: str, device_pref: str) -> str | Exception:
"""Returns the recognized user utterance from the input audio stream.
Parameters:
audio_path: gradio.Audio component.
model_id: String of desired ASR model.
device_preference: String of desired device for ASR processing, "cuda", "cpu", or "auto".
Returns:
hyp_raw: Recognized user utterance.
"""
asr = get_asr_pipeline(model_id, device_pref)
try:
# IMPORTANT: For English-only Whisper (.en), do NOT pass language/task args.
result = asr(audio_path)
hyp_raw = result["text"].strip()
except Exception as e:
return e
return hyp_raw
def similarity_and_diff(ref_tokens: list, hyp_tokens: list) -> (float, list[str, int, int, int]):
"""
Returns:
ratio: Similarity ratio (0..1).
opcodes: List of differences between target and recognized user utterance.
"""
sm = difflib.SequenceMatcher(a=ref_tokens, b=hyp_tokens)
ratio = sm.ratio()
opcodes = sm.get_opcodes()
return ratio, opcodes
class SentenceMatcher:
"""Class for keeping track of (target sentence, user utterance) match features."""
def __init__(self, target_sentence, user_transcript, pass_threshold):
self.target_sentence: str = target_sentence
self.user_transcript: str = user_transcript
self.pass_threshold: float = pass_threshold
self.target_tokens: list = normalize_text(target_sentence).split()
self.user_tokens: list = normalize_text(user_transcript).split()
self.ratio: float
self.alignments: list
self.ratio, self.alignments = similarity_and_diff(self.target_tokens,
self.user_tokens)
self.passed: bool = self.ratio >= self.pass_threshold
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