davidmeikle's picture
Update app.py
04e9646 verified
raw
history blame
6.81 kB
import spaces
import gradio as gr
import torch
import numpy as np
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import platform
import librosa
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
model.to('cuda')
class PhoneticEnhancer:
def __init__(self):
# Vowel length rules
self.long_vowels = {
'i': 'iː',
'u': 'uː',
'a': 'ɑː',
'ɑ': 'ɑː',
'e': 'eː',
'o': 'oː'
}
# Common diphthongs
self.diphthongs = {
'ei': 'eɪ',
'ai': 'aɪ',
'oi': 'ɔɪ',
'ou': 'əʊ',
'au': 'aʊ'
}
# Vowel quality adjustments
self.vowel_quality = {
'ə': 'æ', # In stressed positions
'ɐ': 'æ' # Common substitution
}
# Stress pattern rules
self.stress_patterns = [
# (pattern, position) - position is index from start
(['CV', 'CV'], 1), # For words like "piage"
(['CVV', 'CV'], 0), # For words with long first vowel
]
def _is_vowel(self, phoneme: str) -> bool:
vowels = set('aeiouɑɐəæɛɪʊʌɔ')
return any(char in vowels for char in phoneme)
def _split_into_syllables(self, phonemes: list) -> list:
syllables = []
current_syllable = []
for phoneme in phonemes:
current_syllable.append(phoneme)
if self._is_vowel(phoneme) and len(current_syllable) > 0:
syllables.append(current_syllable)
current_syllable = []
if current_syllable:
if len(syllables) > 0:
syllables[-1].extend(current_syllable)
else:
syllables.append(current_syllable)
return syllables
def enhance_transcription(self, raw_phonemes: str, enhancements: list = None) -> str:
if enhancements is None:
enhancements = ['length', 'quality', 'stress', 'diphthongs']
# Split into individual phonemes
phonemes = raw_phonemes.split()
enhanced_phonemes = phonemes.copy()
if 'length' in enhancements:
# Apply vowel length rules
for i, phoneme in enumerate(enhanced_phonemes):
if phoneme in self.long_vowels:
enhanced_phonemes[i] = self.long_vowels[phoneme]
if 'quality' in enhancements:
# Apply vowel quality adjustments
for i, phoneme in enumerate(enhanced_phonemes):
if phoneme in self.vowel_quality:
enhanced_phonemes[i] = self.vowel_quality[phoneme]
if 'diphthongs' in enhancements:
# Apply diphthong rules
i = 0
while i < len(enhanced_phonemes) - 1:
pair = enhanced_phonemes[i] + enhanced_phonemes[i + 1]
if pair in self.diphthongs:
enhanced_phonemes[i] = self.diphthongs[pair]
enhanced_phonemes.pop(i + 1)
i += 1
if 'stress' in enhancements:
# Add stress marks based on syllable structure
syllables = self._split_into_syllables(enhanced_phonemes)
if len(syllables) > 1:
# Add stress to the syllable containing 'æ' if present
for i, syll in enumerate(syllables):
if any('æ' in p for p in syll):
syllables[i].insert(0, 'ˈ')
break
# If no 'æ', add stress to first syllable by default
else:
syllables[0].insert(0, 'ˈ')
# Flatten syllables back to phonemes
enhanced_phonemes = [p for syll in syllables for p in syll]
return ' '.join(enhanced_phonemes)
def preprocess_audio(audio):
"""Preprocess audio data for model input."""
if isinstance(audio, tuple):
sample_rate, audio_data = audio
else:
return None
if audio_data.dtype != np.float32:
audio_data = audio_data.astype(np.float32)
if audio_data.max() > 1.0 or audio_data.min() < -1.0:
audio_data = audio_data / 32768.0
if len(audio_data.shape) > 1:
audio_data = audio_data.mean(axis=1)
if sample_rate != 16000:
audio_data = librosa.resample(
y=audio_data,
orig_sr=sample_rate,
target_sr=16000
)
return audio_data
@spaces.GPU
def transcribe_to_phonemes(audio, enhancements):
"""Transcribe audio to phonemes with enhancements."""
try:
audio_data = preprocess_audio(audio)
if audio_data is None:
return "Please provide valid audio input"
selected_enhancements = enhancements.split(',') if enhancements else []
inputs = processor(
audio_data,
sampling_rate=16000,
return_tensors="pt",
padding=True
).input_values.to('cuda')
with torch.no_grad():
logits = model(inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
enhancer = PhoneticEnhancer()
enhanced = enhancer.enhance_transcription(
transcription,
selected_enhancements
)
return f"""Raw IPA: {transcription}
Enhanced IPA: {enhanced}
Applied enhancements: {', '.join(selected_enhancements) or 'none'}"""
except Exception as e:
import traceback
return f"Error processing audio: {str(e)}\n{traceback.format_exc()}"
iface = gr.Interface(
fn=transcribe_to_phonemes,
inputs=[
gr.Audio(sources=["microphone", "upload"], type="numpy"),
gr.Textbox(
label="Enhancements (comma-separated)",
value="length,quality,stress,diphthongs",
placeholder="e.g., length,quality,stress,diphthongs"
)
],
outputs="text",
title="Speech to Phoneme Converter - Enhanced IPA",
description="""Convert speech to phonemes with customizable IPA enhancements.
Available enhancements:
- length: Add vowel length markers (ː)
- quality: Adjust vowel quality (e.g., ə → æ)
- stress: Add stress marks (ˈ)
- diphthongs: Combine vowels into diphthongs (e.g., ei → eɪ)
"""
)
iface.launch()