mirror of
https://github.com/SWivid/F5-TTS.git
synced 2026-01-19 00:06:17 -08:00
406 lines
13 KiB
Python
406 lines
13 KiB
Python
import math
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import os
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import random
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import string
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import torch
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import torch.nn.functional as F
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import torchaudio
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from tqdm import tqdm
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from f5_tts.eval.ecapa_tdnn import ECAPA_TDNN_SMALL
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from f5_tts.model.modules import MelSpec
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from f5_tts.model.utils import convert_char_to_pinyin
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# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
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def get_seedtts_testset_metainfo(metalst):
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f = open(metalst)
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lines = f.readlines()
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f.close()
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metainfo = []
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for line in lines:
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if len(line.strip().split("|")) == 5:
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utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
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elif len(line.strip().split("|")) == 4:
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utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
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gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
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if not os.path.isabs(prompt_wav):
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prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
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metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
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return metainfo
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# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
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def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
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f = open(metalst)
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lines = f.readlines()
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f.close()
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metainfo = []
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for line in lines:
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ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
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# ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc)
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ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
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ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
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# gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc)
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gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
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gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
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metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
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return metainfo
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# padded to max length mel batch
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def padded_mel_batch(ref_mels):
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max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
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padded_ref_mels = []
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for mel in ref_mels:
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padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
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padded_ref_mels.append(padded_ref_mel)
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padded_ref_mels = torch.stack(padded_ref_mels)
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padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
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return padded_ref_mels
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# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
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def get_inference_prompt(
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metainfo,
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speed=1.0,
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tokenizer="pinyin",
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polyphone=True,
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target_sample_rate=24000,
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n_fft=1024,
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win_length=1024,
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n_mel_channels=100,
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hop_length=256,
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mel_spec_type="vocos",
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target_rms=0.1,
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use_truth_duration=False,
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infer_batch_size=1,
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num_buckets=200,
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min_secs=3,
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max_secs=40,
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):
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prompts_all = []
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min_tokens = min_secs * target_sample_rate // hop_length
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max_tokens = max_secs * target_sample_rate // hop_length
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batch_accum = [0] * num_buckets
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utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
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[[] for _ in range(num_buckets)] for _ in range(6)
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)
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mel_spectrogram = MelSpec(
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n_fft=n_fft,
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hop_length=hop_length,
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win_length=win_length,
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n_mel_channels=n_mel_channels,
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target_sample_rate=target_sample_rate,
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mel_spec_type=mel_spec_type,
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)
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for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
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# Audio
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ref_audio, ref_sr = torchaudio.load(prompt_wav)
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ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
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if ref_rms < target_rms:
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ref_audio = ref_audio * target_rms / ref_rms
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assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
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if ref_sr != target_sample_rate:
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resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
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ref_audio = resampler(ref_audio)
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# Text
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if len(prompt_text[-1].encode("utf-8")) == 1:
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prompt_text = prompt_text + " "
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text = [prompt_text + gt_text]
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if tokenizer == "pinyin":
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text_list = convert_char_to_pinyin(text, polyphone=polyphone)
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else:
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text_list = text
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# Duration, mel frame length
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ref_mel_len = ref_audio.shape[-1] // hop_length
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if use_truth_duration:
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gt_audio, gt_sr = torchaudio.load(gt_wav)
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if gt_sr != target_sample_rate:
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resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
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gt_audio = resampler(gt_audio)
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total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
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# # test vocoder resynthesis
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# ref_audio = gt_audio
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else:
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ref_text_len = len(prompt_text.encode("utf-8"))
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gen_text_len = len(gt_text.encode("utf-8"))
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total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
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# to mel spectrogram
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ref_mel = mel_spectrogram(ref_audio)
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ref_mel = ref_mel.squeeze(0)
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# deal with batch
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assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
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assert (
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min_tokens <= total_mel_len <= max_tokens
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), f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]."
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bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
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utts[bucket_i].append(utt)
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ref_rms_list[bucket_i].append(ref_rms)
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ref_mels[bucket_i].append(ref_mel)
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ref_mel_lens[bucket_i].append(ref_mel_len)
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total_mel_lens[bucket_i].append(total_mel_len)
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final_text_list[bucket_i].extend(text_list)
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batch_accum[bucket_i] += total_mel_len
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if batch_accum[bucket_i] >= infer_batch_size:
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# print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
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prompts_all.append(
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(
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utts[bucket_i],
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ref_rms_list[bucket_i],
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padded_mel_batch(ref_mels[bucket_i]),
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ref_mel_lens[bucket_i],
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total_mel_lens[bucket_i],
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final_text_list[bucket_i],
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)
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)
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batch_accum[bucket_i] = 0
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(
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utts[bucket_i],
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ref_rms_list[bucket_i],
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ref_mels[bucket_i],
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ref_mel_lens[bucket_i],
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total_mel_lens[bucket_i],
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final_text_list[bucket_i],
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) = [], [], [], [], [], []
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# add residual
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for bucket_i, bucket_frames in enumerate(batch_accum):
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if bucket_frames > 0:
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prompts_all.append(
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(
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utts[bucket_i],
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ref_rms_list[bucket_i],
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padded_mel_batch(ref_mels[bucket_i]),
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ref_mel_lens[bucket_i],
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total_mel_lens[bucket_i],
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final_text_list[bucket_i],
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)
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)
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# not only leave easy work for last workers
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random.seed(666)
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random.shuffle(prompts_all)
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return prompts_all
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# get wav_res_ref_text of seed-tts test metalst
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# https://github.com/BytedanceSpeech/seed-tts-eval
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def get_seed_tts_test(metalst, gen_wav_dir, gpus):
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f = open(metalst)
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lines = f.readlines()
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f.close()
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test_set_ = []
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for line in tqdm(lines):
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if len(line.strip().split("|")) == 5:
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utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
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elif len(line.strip().split("|")) == 4:
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utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
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if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
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continue
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gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
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if not os.path.isabs(prompt_wav):
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prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
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test_set_.append((gen_wav, prompt_wav, gt_text))
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num_jobs = len(gpus)
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if num_jobs == 1:
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return [(gpus[0], test_set_)]
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wav_per_job = len(test_set_) // num_jobs + 1
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test_set = []
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for i in range(num_jobs):
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test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
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return test_set
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# get librispeech test-clean cross sentence test
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def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
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f = open(metalst)
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lines = f.readlines()
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f.close()
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test_set_ = []
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for line in tqdm(lines):
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ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
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if eval_ground_truth:
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gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
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gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
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else:
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if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
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raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
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gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
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ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
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ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
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test_set_.append((gen_wav, ref_wav, gen_txt))
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num_jobs = len(gpus)
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if num_jobs == 1:
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return [(gpus[0], test_set_)]
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wav_per_job = len(test_set_) // num_jobs + 1
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test_set = []
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for i in range(num_jobs):
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test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
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return test_set
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# load asr model
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def load_asr_model(lang, ckpt_dir=""):
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if lang == "zh":
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from funasr import AutoModel
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model = AutoModel(
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model=os.path.join(ckpt_dir, "paraformer-zh"),
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# vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
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# punc_model = os.path.join(ckpt_dir, "ct-punc"),
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# spk_model = os.path.join(ckpt_dir, "cam++"),
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disable_update=True,
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) # following seed-tts setting
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elif lang == "en":
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from faster_whisper import WhisperModel
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model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
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model = WhisperModel(model_size, device="cuda", compute_type="float16")
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return model
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# WER Evaluation, the way Seed-TTS does
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def run_asr_wer(args):
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rank, lang, test_set, ckpt_dir = args
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if lang == "zh":
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import zhconv
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torch.cuda.set_device(rank)
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elif lang == "en":
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os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
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else:
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raise NotImplementedError(
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"lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
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)
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asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
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from zhon.hanzi import punctuation
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punctuation_all = punctuation + string.punctuation
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wers = []
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from jiwer import compute_measures
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for gen_wav, prompt_wav, truth in tqdm(test_set):
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if lang == "zh":
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res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
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hypo = res[0]["text"]
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hypo = zhconv.convert(hypo, "zh-cn")
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elif lang == "en":
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segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
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hypo = ""
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for segment in segments:
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hypo = hypo + " " + segment.text
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# raw_truth = truth
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# raw_hypo = hypo
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for x in punctuation_all:
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truth = truth.replace(x, "")
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hypo = hypo.replace(x, "")
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truth = truth.replace(" ", " ")
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hypo = hypo.replace(" ", " ")
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if lang == "zh":
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truth = " ".join([x for x in truth])
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hypo = " ".join([x for x in hypo])
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elif lang == "en":
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truth = truth.lower()
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hypo = hypo.lower()
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measures = compute_measures(truth, hypo)
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wer = measures["wer"]
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# ref_list = truth.split(" ")
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# subs = measures["substitutions"] / len(ref_list)
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# dele = measures["deletions"] / len(ref_list)
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# inse = measures["insertions"] / len(ref_list)
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wers.append(wer)
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return wers
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# SIM Evaluation
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def run_sim(args):
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rank, test_set, ckpt_dir = args
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device = f"cuda:{rank}"
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model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
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state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
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model.load_state_dict(state_dict["model"], strict=False)
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use_gpu = True if torch.cuda.is_available() else False
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if use_gpu:
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model = model.cuda(device)
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model.eval()
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sim_list = []
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for wav1, wav2, truth in tqdm(test_set):
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wav1, sr1 = torchaudio.load(wav1)
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wav2, sr2 = torchaudio.load(wav2)
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resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
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resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
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wav1 = resample1(wav1)
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wav2 = resample2(wav2)
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if use_gpu:
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wav1 = wav1.cuda(device)
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wav2 = wav2.cuda(device)
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with torch.no_grad():
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emb1 = model(wav1)
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emb2 = model(wav2)
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sim = F.cosine_similarity(emb1, emb2)[0].item()
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# print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
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sim_list.append(sim)
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return sim_list
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