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Handling invalid audio generations (for DPO) #43

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merged 6 commits into from
Jan 30, 2025
Merged

Handling invalid audio generations (for DPO) #43

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de197f0
test_step(): handle invalid audio
rfejgin Jan 27, 2025
275d51c
DPO: changes to preference pair creation
rfejgin Jan 27, 2025
2524a27
Comment
rfejgin Jan 27, 2025
7709f33
Merge remote-tracking branch 'paarth/experimentalt5tts_finalizedtrans…
rfejgin Jan 30, 2025
09ab0aa
Fix merge issues and a bug
rfejgin Jan 30, 2025
33a0f3e
Fix merge issues
rfejgin Jan 30, 2025
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63 changes: 40 additions & 23 deletions nemo/collections/tts/models/t5tts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1062,6 +1062,7 @@ def test_step(self, batch, batch_idx):
)
predicted_audio_paths = []
audio_durations = []
batch_invalid = False
for idx in range(predicted_audio.size(0)):
predicted_audio_np = predicted_audio[idx].float().detach().cpu().numpy()
predicted_audio_np = predicted_audio_np[:predicted_audio_lens[idx]]
Expand All @@ -1079,33 +1080,49 @@ def test_step(self, batch, batch_idx):
predicted_codes_torch = predicted_codes_torch[:, :predicted_codes_lens[idx]]
torch.save(predicted_codes_torch, os.path.join(audio_dir, f'predicted_audioRank{self.global_rank}_{item_idx}_codes.pt'))
predicted_audio_paths.append(audio_path)

with torch.no_grad():
if self.cfg.get("pref_set_language", "en") == "en":
pred_transcripts = self.eval_asr_model.transcribe(predicted_audio_paths, batch_size=len(predicted_audio_paths))[0]
pred_transcripts = [ self.process_text(transcript) for transcript in pred_transcripts ]
else:
pred_transcripts = [self.transcribe_with_whisper(audio_path, self.cfg.pref_set_language) for audio_path in predicted_audio_paths]
pred_transcripts = [self.process_text(transcript) for transcript in pred_transcripts]
pred_speaker_embeddings = self.get_speaker_embeddings_from_filepaths(predicted_audio_paths)
gt_speaker_embeddings = self.get_speaker_embeddings_from_filepaths(batch['audio_filepaths'])

if not batch_invalid:
with torch.no_grad():
try:
if self.cfg.get("pref_set_language", "en") == "en":
pred_transcripts = self.eval_asr_model.transcribe(predicted_audio_paths, batch_size=len(predicted_audio_paths))[0]
pred_transcripts = [ self.process_text(transcript) for transcript in pred_transcripts ]
else:
pred_transcripts = [self.transcribe_with_whisper(audio_path, self.cfg.pref_set_language) for audio_path in predicted_audio_paths]
pred_transcripts = [self.process_text(transcript) for transcript in pred_transcripts]
except Exception as e:
assert (predicted_audio_lens[idx] < 1000).any(), f"Expected short audio file to be the only cause of ASR errors, but got error with lengths {predicted_audio_lens}"
logging.warning(f"Exception during ASR transcription: {e}")
logging.warning(f"Skipping processing of the batch; generating metrics indicating a WER of 100% and Speaker Similarity of 0.0")
batch_invalid = True
continue # don't break since we want to continue building audio durations list
pred_speaker_embeddings = self.get_speaker_embeddings_from_filepaths(predicted_audio_paths)
gt_speaker_embeddings = self.get_speaker_embeddings_from_filepaths(batch['audio_filepaths'])

for idx in range(predicted_audio.size(0)):
audio_path = predicted_audio_paths[idx]
item_idx = batch_idx * test_dl_batch_size + idx
pred_transcript = pred_transcripts[idx]
gt_transcript = self.process_text(batch['raw_texts'][idx])
if not batch_invalid:
audio_path = predicted_audio_paths[idx]
item_idx = batch_idx * test_dl_batch_size + idx
pred_transcript = pred_transcripts[idx]
gt_transcript = self.process_text(batch['raw_texts'][idx])

cer_gt = word_error_rate([pred_transcript], [gt_transcript], use_cer=True)
wer_gt = word_error_rate([pred_transcript], [gt_transcript], use_cer=False)
cer_gt = word_error_rate([pred_transcript], [gt_transcript], use_cer=True)
wer_gt = word_error_rate([pred_transcript], [gt_transcript], use_cer=False)

spk_embedding_pred = pred_speaker_embeddings[idx].cpu().numpy()
spk_embedding_gt = gt_speaker_embeddings[idx].cpu().numpy()

spk_similarity = np.dot(spk_embedding_pred, spk_embedding_gt) / (
np.linalg.norm(spk_embedding_pred) * np.linalg.norm(spk_embedding_gt)
)
else:
# Create an entry indicating invalid metrics
cer_gt = 1.0
wer_gt = 1.0
spk_similarity = 0.0
pred_transcript = "<INVALID>"
gt_transcript = self.process_text(batch['raw_texts'][idx])

spk_embedding_pred = pred_speaker_embeddings[idx].cpu().numpy()
spk_embedding_gt = gt_speaker_embeddings[idx].cpu().numpy()

spk_similarity = np.dot(spk_embedding_pred, spk_embedding_gt) / (
np.linalg.norm(spk_embedding_pred) * np.linalg.norm(spk_embedding_gt)
)

item_metrics = {
'cer_gt': float(cer_gt),
'wer_gt': float(wer_gt),
Expand Down
14 changes: 12 additions & 2 deletions scripts/t5tts/dpo/create_preference_pairs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import copy
import random
import math
from tqdm import tqdm

def main():
parser = argparse.ArgumentParser()
Expand All @@ -18,7 +19,7 @@ def main():
audio_files, codec_files, metric_files = find_audio_files(args.generated_audio_dir)
assert len(records) <= len(audio_files), "Mismatch between number of records and number of generated audio files {} vs {}".format(len(records), len(audio_files))

for idx, record in enumerate(records):
for idx, record in tqdm(enumerate(records)):
if idx % 100 == 0:
print("At idx: ", idx, len(records))
record['audio_filepath'] = audio_files[idx]
Expand Down Expand Up @@ -187,6 +188,7 @@ def create_chosen_rejected_records(records_orig, group_size=6, num_chosen_per_gr
num_groups = len(records) // group_size
best_records = []
worst_records = []
num_skipped = 0

if num_chosen_per_group == 1:
chosen_group_indices = [0]
Expand All @@ -203,9 +205,16 @@ def create_chosen_rejected_records(records_orig, group_size=6, num_chosen_per_gr
group = records[gsi:gei]

cer_sim_indices = []
skip_group = False
for sidx, record in enumerate(group):
if record['pred_transcript'] == "<INVALID>":
print(f"Skipping group starting at index {gsi} due to invalid entries.")
num_skipped += len(group)
skip_group = True
break
cer_sim_indices.append((record['cer_gts'], record['pred_context_similarity'], sidx))

if skip_group:
continue
cer_sim_indices_orig = copy.deepcopy(cer_sim_indices)
cer_sim_indices = pareto_rank(cer_sim_indices)

Expand All @@ -228,6 +237,7 @@ def create_chosen_rejected_records(records_orig, group_size=6, num_chosen_per_gr
best_records.append(best_record)
worst_records.append(worst_record)

print(f"Skipped {num_skipped} records due to invalid entries.")
return best_records, worst_records

def filter_best_and_worst_records(best_records, worst_records, cer_threshold=0.02):
Expand Down
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