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import os
import re
import numpy as np
import torch
import torchaudio
MAX_CHANNELS = 8
def pad_or_truncate_to_seconds(
wav: torch.Tensor, target_seconds: float, sr: int
) -> torch.Tensor:
"""Pad or truncate a mono waveform to target length in seconds.
Args:
wav: (1, T) or (T,) tensor
target_seconds: target duration in seconds
sr: sample rate
Returns:
(1, T_target) tensor
"""
if wav.dim() == 2 and wav.shape[0] == 1:
wav_1d = wav.squeeze(0)
else:
wav_1d = wav.reshape(-1)
target_len = int(round(target_seconds * sr))
cur_len = wav_1d.shape[-1]
if cur_len == target_len:
out = wav_1d
elif cur_len > target_len:
out = wav_1d[:target_len]
else:
pad_len = target_len - cur_len
out = torch.cat(
[wav_1d, torch.zeros(pad_len, dtype=wav_1d.dtype, device=wav_1d.device)],
dim=-1,
)
return out.unsqueeze(0)
def crossfade_concat(
segments: list, sample_rate: int, crossfade_seconds: float = 0.1
) -> torch.Tensor:
"""Concatenate segments with linear crossfade.
Args:
segments: list of (1, T) tensors
sample_rate: sampling rate
crossfade_seconds: overlap time for crossfade
Returns:
(1, T_total) tensor
"""
if len(segments) == 0:
return torch.zeros(1, 0)
if len(segments) == 1:
return segments[0]
out = segments[0]
cf_len_target = int(round(crossfade_seconds * sample_rate))
for k in range(1, len(segments)):
nxt = segments[k]
if cf_len_target <= 0:
out = torch.cat([out, nxt], dim=-1)
continue
cf_len = min(cf_len_target, out.shape[-1], nxt.shape[-1])
if cf_len <= 0:
out = torch.cat([out, nxt], dim=-1)
continue
fade_out = torch.linspace(
1.0, 0.0, steps=cf_len, dtype=out.dtype, device=out.device
)
fade_in = torch.linspace(
0.0, 1.0, steps=cf_len, dtype=nxt.dtype, device=nxt.device
)
overlap = out[0, -cf_len:] * fade_out + nxt[0, :cf_len] * fade_in
out = torch.cat(
[out[:, :-cf_len], overlap.unsqueeze(0), nxt[:, cf_len:]], dim=-1
)
return out
def load_model(
model_path,
spt_config_path,
spt_checkpoint_path,
torch_dtype=torch.bfloat16,
attn_implementation="sdpa",
):
from transformers import AutoTokenizer
from modeling_asteroid import AsteroidTTSInstruct
from XY_Tokenizer.xy_tokenizer.model import XY_Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AsteroidTTSInstruct.from_pretrained(
model_path, torch_dtype=torch_dtype, attn_implementation=attn_implementation
)
spt = XY_Tokenizer.load_from_checkpoint(
config_path=spt_config_path, ckpt_path=spt_checkpoint_path
)
model.eval()
spt.eval()
return tokenizer, model, spt
def process_jsonl_item(item):
"""Parse a JSONL item enforcing prompt requirement.
Only supports Format 1 (separate speaker refs) and Format 2 (shared ref),
consistent with the updated README. If `base_path` is missing/empty, any
string paths must be absolute. Text-only input is not supported and will raise.
"""
base_path = item.get("base_path", "") or ""
text = item.get("text", "")
def _resolve_path(p: str) -> str:
if not isinstance(p, str) or not p:
return p
if base_path:
return os.path.join(base_path, p)
# base_path missing: require absolute path
if not os.path.isabs(p):
raise ValueError(
"When base_path is omitted, audio paths must be absolute. Got: " + p
)
return p
# Try Format 2 first: shared audio reference
prompt_audio = None
prompt_text = ""
if "prompt_audio" in item:
prompt_audio_val = item.get("prompt_audio")
if not prompt_audio_val:
raise ValueError("Format 2 requires non-empty 'prompt_audio'.")
if isinstance(prompt_audio_val, str):
prompt_audio = _resolve_path(prompt_audio_val)
else:
# allow tuple form for backward-compatibility
prompt_audio = prompt_audio_val
prompt_text = item.get("prompt_text", "")
return {"text": text, "prompt_text": prompt_text, "prompt_audio": prompt_audio}
# Try Format 1: separate speaker references
s1 = item.get("prompt_audio_speaker1", "")
s2 = item.get("prompt_audio_speaker2", "")
has_s1 = (isinstance(s1, str) and s1) or isinstance(s1, tuple)
has_s2 = (isinstance(s2, str) and s2) or isinstance(s2, tuple)
if has_s1 and has_s2:
if isinstance(s1, str) and s1:
s1_resolved = _resolve_path(s1)
else:
s1_resolved = s1
if isinstance(s2, str) and s2:
s2_resolved = _resolve_path(s2)
else:
s2_resolved = s2
# Build merged prompt audio dict
prompt_audio = {"speaker1": s1_resolved, "speaker2": s2_resolved}
# Merge texts
pt1 = item.get("prompt_text_speaker1", "")
pt2 = item.get("prompt_text_speaker2", "")
merged = ""
if pt1:
merged += f"[S1]{pt1}"
if pt2:
merged += f"[S2]{pt2}"
prompt_text = merged.strip()
return {"text": text, "prompt_text": prompt_text, "prompt_audio": prompt_audio}
# Otherwise, no supported prompt found β reject (text-only unsupported)
raise ValueError(
"Input must include prompt (Format 1 or 2). Text-only is not supported."
)
def load_audio_data(prompt_audio, target_sample_rate=16000):
"""Load audio data and return processed audio tensor
Args:
prompt_audio: Can be in the following formats:
- String: audio file path
- Tuple: (wav, sr) result from torchaudio.load
- Dict: {"speaker1": path_or_tuple, "speaker2": path_or_tuple}
"""
if prompt_audio is None:
return None
try:
# Check if prompt_audio is a dictionary (containing speaker1 and speaker2)
if (
isinstance(prompt_audio, dict)
and "speaker1" in prompt_audio
and "speaker2" in prompt_audio
):
# Process audio from both speakers separately
wav1, sr1 = _load_single_audio(prompt_audio["speaker1"])
wav2, sr2 = _load_single_audio(prompt_audio["speaker2"])
# Merge audio from both speakers
wav = merge_speaker_audios(wav1, sr1, wav2, sr2, target_sample_rate)
if wav is None:
return None
else:
# Single audio
wav, sr = _load_single_audio(prompt_audio)
# Resample to 16k
if sr != target_sample_rate:
wav = torchaudio.functional.resample(wav, sr, target_sample_rate)
# Ensure mono channel
if wav.shape[0] > 1:
wav = wav.mean(dim=0, keepdim=True) # Convert multi-channel to mono
if len(wav.shape) == 1:
wav = wav.unsqueeze(0)
return wav
except Exception as e:
print(f"Error loading audio data: {e}")
raise
def _load_single_audio(audio_input):
"""Load single audio, supports file path or (wav, sr) tuple
Args:
audio_input: String (file path) or tuple (wav, sr)
Returns:
tuple: (wav, sr)
"""
if isinstance(audio_input, tuple) and len(audio_input) == 2:
# Already a (wav, sr) tuple
wav, sr = audio_input
return wav, sr
elif isinstance(audio_input, str):
# Is a file path, needs to be loaded
wav, sr = torchaudio.load(audio_input)
return wav, sr
else:
raise ValueError(f"Unsupported audio input format: {type(audio_input)}")
def merge_speaker_audios(wav1, sr1, wav2, sr2, target_sample_rate=16000):
"""Merge audio data from two speakers"""
try:
# Process first audio
if sr1 != target_sample_rate:
wav1 = torchaudio.functional.resample(wav1, sr1, target_sample_rate)
# Ensure mono channel
if wav1.shape[0] > 1:
wav1 = wav1.mean(dim=0, keepdim=True) # Convert multi-channel to mono
if len(wav1.shape) == 1:
wav1 = wav1.unsqueeze(0)
# Process second audio
if sr2 != target_sample_rate:
wav2 = torchaudio.functional.resample(wav2, sr2, target_sample_rate)
# Ensure mono channel
if wav2.shape[0] > 1:
wav2 = wav2.mean(dim=0, keepdim=True) # Convert multi-channel to mono
if len(wav2.shape) == 1:
wav2 = wav2.unsqueeze(0)
# Concatenate audio
merged_wav = torch.cat([wav1, wav2], dim=1)
return merged_wav
except Exception as e:
print(f"Error merging audio: {e}")
raise
def process_inputs(
tokenizer,
spt,
prompt,
text,
device,
silence_duration,
audio_data=None,
max_channels=8,
pad_token=1024,
):
seq = f"<|begin_of_style|>{prompt}<|end_of_style|>\n<|begin_of_text|>{text}<|end_of_text|>\n<|begin_of_speech|>"
inputs1 = np.array(tokenizer.encode(seq))
input_ids = np.full((inputs1.shape[0], max_channels), pad_token)
input_ids[:, 0] = inputs1
if audio_data is not None:
try:
# audio_data should now be a processed audio tensor
wav = audio_data
# Add fixed 5-second silence at the end of audio (using 16k sample rate)
silence_samples = int(silence_duration * 16000)
silence = torch.zeros(wav.shape[0], silence_samples)
wav = torch.cat([wav, silence], dim=1)
with torch.no_grad():
# Use SPT encoding
encode_result = spt.encode([wav.squeeze().to(device)])
audio_token = (
encode_result["codes_list"][0].permute(1, 0).cpu().numpy()
) # Adjust dimension order
# similar to DAC encoding adjustment
audio_token[:, 0] = (
audio_token[:, 0] + 151665
) # Keep this line if offset is needed, otherwise delete
input_ids = np.concatenate([input_ids, audio_token])
except Exception as e:
print(f"Error processing audio data: {e}")
raise
return input_ids
def shifting_inputs(input_ids, tokenizer, pad_token=1024, max_channels=8):
seq_len = input_ids.shape[0]
new_seq_len = seq_len + max_channels - 1
shifted_input_ids = np.full((new_seq_len, max_channels), pad_token, dtype=np.int64)
shifted_input_ids[:, 0] = np.full(
new_seq_len, tokenizer.pad_token_id, dtype=np.int64
)
for i in range(max_channels):
shifted_input_ids[i : (seq_len + i), i] = input_ids[:, i]
return shifted_input_ids
def rpadding(input_ids, channels, tokenizer):
attention_masks = [np.ones(inputs.shape[0]) for inputs in input_ids]
max_length = max(ids.shape[0] for ids in input_ids)
padded_input_ids, padded_attns = [], []
for ids, attn in zip(input_ids, attention_masks):
pad_len = max_length - ids.shape[0]
input_pad = np.full((pad_len, channels), 1024)
input_pad[:, 0] = tokenizer.pad_token_id
padded_input_ids.append(np.concatenate([input_pad, ids]))
attn_pad = np.zeros(pad_len)
padded_attns.append(np.concatenate([attn_pad, attn]))
input_ids = torch.tensor(np.stack(padded_input_ids))
attention_mask = torch.tensor(np.stack(padded_attns))
return input_ids, attention_mask
def find_max_valid_positions(C: torch.Tensor, invalid_value=1024) -> torch.Tensor:
values = C[:, :, 1]
mask = (values != invalid_value)
reversed_mask = mask.flip(dims=[1])
reversed_indices = torch.argmax(reversed_mask.int(), dim=1)
seq_len = C.size(1)
original_indices = seq_len - 1 - reversed_indices
has_valid = mask.any(dim=1)
original_indices = torch.where(has_valid, original_indices, -1)
return original_indices
def normalize_text(text: str) -> str:
"""
Normalize multi-speaker script.
1. Don't preserve line breaks.
2. Preserve bracketed segments like [] () <> even when they are not speaker tags.
3. Remove decorative symbols: γγγγοΌοΌγγγγο½~-_.
4. Internal punctuation οΌοΌγ β οΌοΌkeep οΌοΌ?.
5. Multiple γ keep only the last one, others β οΌγ
6. Replace consecutive "ε" (>=2) with "(η¬)".
7. Auto-recognize [S1] / [S2] β¦ tags; if missing, treat as whole segment.
8. Merge adjacent identical speaker tags.
"""
# Replace [1], [2] etc. format with [S1], [S2] etc. format
text = re.sub(r"\[(\d+)\]", r"[S\1]", text)
# Remove decorative characters
remove_chars = "γγγγοΌοΌγγγγ" '"-_ββο½~ββ'
# Use positive lookahead to split text by speaker tags (tags themselves are still preserved)
segments = re.split(r"(?=\[S\d+\])", text.replace("\n", " "))
processed_parts = []
for seg in segments:
seg = seg.strip()
if not seg:
continue
# Extract tags
m = re.match(r"^(\[S\d+\])\s*(.*)", seg)
tag, content = m.groups() if m else ("", seg)
# Remove irrelevant symbols
content = re.sub(f"[{re.escape(remove_chars)}]", "", content)
# Handle consecutive "ε" characters: replace 2 or more with "(η¬)"
content = re.sub(r"ε{2,}", "[η¬]", content)
# Handle English laughter (e.g., "haha", "ha ha")
content = re.sub(r"\b(ha(\s*ha)+)\b", "[laugh]", content, flags=re.IGNORECASE)
# First handle multi-character punctuation marks
content = content.replace("ββ", "οΌ")
content = content.replace("β¦β¦", "οΌ")
# Handle single-character internal punctuation marks
internal_punct_map = str.maketrans(
{"οΌ": "οΌ", ";": ",", "οΌ": "οΌ", ":": ",", "γ": "οΌ"}
)
content = content.translate(internal_punct_map)
content = content.strip()
# Keep only the final period
if len(content) > 1:
last_ch = (
"γ"
if content[-1] == "οΌ"
else ("." if content[-1] == "," else content[-1])
)
body = content[:-1].replace("γ", "οΌ")
content = body + last_ch
processed_parts.append({"tag": tag, "content": content})
if not processed_parts:
return ""
# Merge consecutive same speakers
merged_lines = []
current_tag = processed_parts[0]["tag"]
current_content = [processed_parts[0]["content"]]
for part in processed_parts[1:]:
if part["tag"] == current_tag and current_tag:
current_content.append(part["content"])
else:
merged_lines.append(f"{current_tag}{''.join(current_content)}".strip())
current_tag = part["tag"]
current_content = [part["content"]]
merged_lines.append(f"{current_tag}{''.join(current_content)}".strip())
return "".join(merged_lines).replace("β", "'").replace("β", "'")
def process_batch(
batch_items,
tokenizer,
model,
spt,
device,
system_prompt,
start_idx,
use_normalize=False,
silence_duration=0,
):
"""Process a batch of data items and generate audio, return audio data and metadata"""
try:
# Prepare batch data
batch_size = len(batch_items)
texts = []
prompts = [system_prompt] * batch_size
prompt_audios = []
actual_texts_data = [] # Store actual text data used
print(f"Processing {batch_size} samples starting from index {start_idx}...")
# Extract text and audio from each sample
for i, item in enumerate(batch_items):
# Use new processing function
processed_item = process_jsonl_item(item)
text = processed_item["text"]
prompt_text = processed_item["prompt_text"]
# Merge text, if prompt_text is empty, full_text is just text
full_text = prompt_text + text if prompt_text else text
original_full_text = full_text # Save original text
# Apply text normalization based on parameter
if use_normalize:
full_text = normalize_text(full_text)
# Replace speaker tags
final_text = full_text.replace("[S1]", "<speaker1>").replace(
"[S2]", "<speaker2>"
)
texts.append(final_text)
# Save actual text information used
actual_texts_data.append(
{
"index": start_idx + i,
"original_text": original_full_text,
"normalized_text": (
normalize_text(original_full_text) if use_normalize else None
),
"final_text": final_text,
"use_normalize": use_normalize,
}
)
# Get reference audio
prompt_audios.append(processed_item["prompt_audio"])
# Process inputs
input_ids_list = []
for i, (text, prompt, audio_path) in enumerate(
zip(texts, prompts, prompt_audios)
):
# Load audio data here
audio_data = load_audio_data(audio_path) if audio_path else None
inputs = process_inputs(
tokenizer, spt, prompt, text, device, silence_duration, audio_data
)
inputs = shifting_inputs(inputs, tokenizer)
input_ids_list.append(inputs)
# Pad batch inputs
input_ids, attention_mask = rpadding(input_ids_list, MAX_CHANNELS, tokenizer)
# Batch generation
print(f"Starting batch audio generation...")
start = input_ids.shape[1] - MAX_CHANNELS + 1
# Move inputs to GPU
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
# Generate model outputs
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
)
print(f"Original outputs shape: {outputs.shape}")
print(f"Start value: {start}")
print(f"Shape after slicing: {outputs[:, start:].shape}")
print(f"MAX_CHANNELS: {MAX_CHANNELS}")
print(f"Calculated seq_len: {outputs.shape[1] - MAX_CHANNELS + 1}")
# Process outputs
outputs = outputs[:, start:]
seq_len = outputs.shape[1] - MAX_CHANNELS + 1
speech_ids = torch.full((outputs.shape[0], seq_len, MAX_CHANNELS), 0).to(device)
# Adjust output format
for j in range(MAX_CHANNELS):
speech_ids[..., j] = outputs[:, j : seq_len + j, j]
if j == 0:
speech_ids[..., j] = speech_ids[..., j] - 151665
# Find valid positions for each sample
li = find_max_valid_positions(speech_ids)
# Store audio result data
audio_results = []
# Process batch sample results individually
for i in range(batch_size):
try:
# Extract valid speech tokens
end_idx = li[i] + 1
if end_idx <= 0:
print(f"Sample {start_idx + i} has no valid speech tokens")
audio_results.append(None)
continue
this_speech_id = speech_ids[i, :end_idx]
print(
f"Speech token shape for sample {start_idx + i}: {this_speech_id.shape}"
)
# Prompt-Augmented Decode (rvq8-style); fall back to original decode if no prompt
prompt_audio = prompt_audios[i]
if prompt_audio is None:
# Fallback to original decode
with torch.no_grad():
codes_list = [this_speech_id.permute(1, 0)]
decode_result = spt.decode(codes_list, overlap_seconds=10)
audio_out = decode_result["syn_wav_list"][0].cpu().detach()
if audio_out.ndim == 1:
audio_out = audio_out.unsqueeze(0)
audio_results.append(
{
"audio_data": audio_out,
"sample_rate": spt.output_sample_rate,
"index": start_idx + i,
}
)
print(f"Audio generation completed (orig): sample {start_idx + i}")
else:
# 1) Load prompt at SPT input sr and force to 20s
ref_sr_in = (
getattr(spt, "input_sample_rate", None)
or getattr(spt, "sampling_rate", None)
or 24000
)
ref_wav = load_audio_data(
prompt_audio, target_sample_rate=ref_sr_in
)
if ref_wav is None:
# If ref missing, use original decode
with torch.no_grad():
codes_list = [this_speech_id.permute(1, 0)]
decode_result = spt.decode(codes_list, overlap_seconds=10)
audio_out = decode_result["syn_wav_list"][0].cpu().detach()
if audio_out.ndim == 1:
audio_out = audio_out.unsqueeze(0)
audio_results.append(
{
"audio_data": audio_out,
"sample_rate": spt.output_sample_rate,
"index": start_idx + i,
}
)
print(
f"Audio generation completed (orig no-ref): sample {start_idx + i}"
)
else:
# Encode 20s reference to tokens
ref_wav_20s = pad_or_truncate_to_seconds(
ref_wav, 20.0, ref_sr_in
).to(device)
with torch.no_grad():
enc = spt.encode([ref_wav_20s.squeeze(0)])
ref_codes = (
enc["codes_list"][0].to(device).long()
) # (nq, T_ref)
# Prepare token-to-sample mapping and windowing params
out_sr = (
getattr(spt, "output_sample_rate", None)
or getattr(spt, "sample_rate", None)
or 24000
)
tokens_per_second = float(ref_sr_in) / float(
spt.encoder_downsample_rate
)
tokens_per_chunk = int(round(10.0 * tokens_per_second))
stride_tokens = 85
keep_tokens = 85
left_ctx_tokens = 20
total_tokens = this_speech_id.shape[0]
samples_per_token = int(round(out_sr / tokens_per_second))
crossfade_seconds = 0.1
crossfade_samples = int(round(crossfade_seconds * out_sr))
kept_segments = []
chunk_idx = 0
while True:
st_tok = chunk_idx * stride_tokens
if st_tok >= total_tokens:
break
ed_tok = min(st_tok + tokens_per_chunk, total_tokens)
gen_chunk = this_speech_id[st_tok:ed_tok] # (len, C)
if gen_chunk.shape[0] == 0:
break
# Concatenate reference tokens with current window tokens
combined_codes = torch.cat(
[ref_codes, gen_chunk.permute(1, 0).long()], dim=1
).to(
device
) # (nq, T_ref + T_chunk)
codes_lengths = torch.tensor(
[combined_codes.shape[-1]],
dtype=torch.long,
device=device,
)
combined_codes_batched = combined_codes.unsqueeze(
1
) # (nq, 1, T)
with torch.no_grad():
detok = spt.inference_detokenize(
combined_codes_batched, codes_lengths
)
y = detok["y"][0, 0] # (T_samples)
# Remove 20s reference portion (in samples)
ref_samples = int(round(20.0 * out_sr))
if y.shape[-1] <= ref_samples:
chunk_idx += 1
continue
chunk_y = y[ref_samples:]
# Determine kept region within current window
window_len = gen_chunk.shape[0]
remains = total_tokens - st_tok
is_first = chunk_idx == 0
is_last = ed_tok >= total_tokens
if is_first:
keep_start_tok = 0
keep_end_tok = min(
keep_tokens + left_ctx_tokens, window_len
)
elif is_last and remains < 105:
keep_start_tok = (
0 if is_first else min(left_ctx_tokens, window_len)
)
keep_end_tok = window_len
else:
keep_start_tok = min(left_ctx_tokens, window_len)
keep_end_tok = min(
left_ctx_tokens + keep_tokens, window_len
)
keep_start_smps = keep_start_tok * samples_per_token
keep_end_smps = keep_end_tok * samples_per_token
left_margin = 0
right_margin = crossfade_samples if not is_last else 0
seg_start = max(0, keep_start_smps - left_margin)
seg_end = min(
chunk_y.shape[-1], keep_end_smps + right_margin
)
if seg_end > seg_start:
kept_segments.append(
chunk_y[seg_start:seg_end]
.detach()
.cpu()
.unsqueeze(0)
)
chunk_idx += 1
# Concatenate with crossfade; if empty, return tiny silence
if len(kept_segments) == 0:
audio_out = torch.zeros(1, int(0.01 * out_sr))
else:
audio_out = crossfade_concat(
kept_segments,
out_sr,
crossfade_seconds=crossfade_seconds,
)
audio_results.append(
{
"audio_data": audio_out,
"sample_rate": out_sr,
"index": start_idx + i,
}
)
print(
f"Audio generation completed (prompt-aug): sample {start_idx + i}"
)
except Exception as e:
print(f"Error processing sample {start_idx + i}: {str(e)}, skipping...")
import traceback
traceback.print_exc()
audio_results.append(None)
# Clean up GPU memory
torch.cuda.empty_cache()
# Return text data and audio data
return actual_texts_data, audio_results
except Exception as e:
print(f"Error during batch processing: {str(e)}")
raise |