support gpt-sovits v4

support gpt-sovits v4

GPT_SoVITS/module/data_utils.py

@@ -470,6 +470,216 @@ class TextAudioSpeakerCollateV3:
         # return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
         return ssl_padded, spec_padded, mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, mel_lengths
 
+class TextAudioSpeakerLoaderV4(torch.utils.data.Dataset):
+    """
+    1) loads audio, speaker_id, text pairs
+    2) normalizes text and converts them to sequences of integers
+    3) computes spectrograms from audio files.
+    """
+
+    def __init__(self, hparams, val=False):
+        exp_dir = hparams.exp_dir
+        self.path2 = "%s/2-name2text.txt" % exp_dir
+        self.path4 = "%s/4-cnhubert" % exp_dir
+        self.path5 = "%s/5-wav32k" % exp_dir
+        assert os.path.exists(self.path2)
+        assert os.path.exists(self.path4)
+        assert os.path.exists(self.path5)
+        names4 = set([name[:-3] for name in list(os.listdir(self.path4))])  # 去除.pt后缀
+        names5 = set(os.listdir(self.path5))
+        self.phoneme_data = {}
+        with open(self.path2, "r", encoding="utf8") as f:
+            lines = f.read().strip("\n").split("\n")
+
+        for line in lines:
+            tmp = line.split("\t")
+            if len(tmp) != 4:
+                continue
+            self.phoneme_data[tmp[0]] = [tmp[1]]
+
+        self.audiopaths_sid_text = list(set(self.phoneme_data) & names4 & names5)
+        tmp = self.audiopaths_sid_text
+        leng = len(tmp)
+        min_num = 100
+        if leng < min_num:
+            self.audiopaths_sid_text = []
+            for _ in range(max(2, int(min_num / leng))):
+                self.audiopaths_sid_text += tmp
+        self.max_wav_value = hparams.max_wav_value
+        self.sampling_rate = hparams.sampling_rate
+        self.filter_length = hparams.filter_length
+        self.hop_length = hparams.hop_length
+        self.win_length = hparams.win_length
+        self.sampling_rate = hparams.sampling_rate
+        self.val = val
+
+        random.seed(1234)
+        random.shuffle(self.audiopaths_sid_text)
+
+        print("phoneme_data_len:", len(self.phoneme_data.keys()))
+        print("wav_data_len:", len(self.audiopaths_sid_text))
+
+        audiopaths_sid_text_new = []
+        lengths = []
+        skipped_phone = 0
+        skipped_dur = 0
+        for audiopath in tqdm(self.audiopaths_sid_text):
+            try:
+                phoneme = self.phoneme_data[audiopath][0]
+                phoneme = phoneme.split(" ")
+                phoneme_ids = cleaned_text_to_sequence(phoneme, version)
+            except Exception:
+                print(f"{audiopath} not in self.phoneme_data !")
+                skipped_phone += 1
+                continue
+
+            size = os.path.getsize("%s/%s" % (self.path5, audiopath))
+            duration = size / self.sampling_rate / 2
+
+            if duration == 0:
+                print(f"Zero duration for {audiopath}, skipping...")
+                skipped_dur += 1
+                continue
+
+            if 54 > duration > 0.6 or self.val:
+                audiopaths_sid_text_new.append([audiopath, phoneme_ids])
+                lengths.append(size // (2 * self.hop_length))
+            else:
+                skipped_dur += 1
+                continue
+
+        print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
+        print("total left: ", len(audiopaths_sid_text_new))
+        assert len(audiopaths_sid_text_new) > 1  # 至少能凑够batch size，这里todo
+        self.audiopaths_sid_text = audiopaths_sid_text_new
+        self.lengths = lengths
+        self.spec_min = -12
+        self.spec_max = 2
+
+        self.filter_length_mel = self.win_length_mel = 1280
+        self.hop_length_mel = 320
+        self.n_mel_channels = 100
+        self.sampling_rate_mel = 32000
+        self.mel_fmin = 0
+        self.mel_fmax = None
+
+    def norm_spec(self, x):
+        return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1
+
+    def get_audio_text_speaker_pair(self, audiopath_sid_text):
+        audiopath, phoneme_ids = audiopath_sid_text
+        text = torch.FloatTensor(phoneme_ids)
+        try:
+            spec, mel = self.get_audio("%s/%s" % (self.path5, audiopath))
+            with torch.no_grad():
+                ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu")
+                if ssl.shape[-1] != spec.shape[-1]:
+                    typee = ssl.dtype
+                    ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
+                ssl.requires_grad = False
+        except:
+            traceback.print_exc()
+            mel = torch.zeros(100, 192)
+            # wav = torch.zeros(1, 96 * self.hop_length)
+            spec = torch.zeros(1025, 96)
+            ssl = torch.zeros(1, 768, 96)
+            text = text[-1:]
+            print("load audio or ssl error!!!!!!", audiopath)
+        return (ssl, spec, mel, text)
+
+    def get_audio(self, filename):
+        audio_array = load_audio(filename, self.sampling_rate)  # load_audio的方法是已经归一化到-1~1之间的，不用再/32768
+        audio = torch.FloatTensor(audio_array)  # /32768
+        audio_norm = audio
+        audio_norm = audio_norm.unsqueeze(0)
+        spec = spectrogram_torch(
+            audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length, center=False
+        )
+        spec = torch.squeeze(spec, 0)
+        spec1 = spectrogram_torch(audio_norm, 1280,32000, 320, 1280,center=False)
+        mel = spec_to_mel_torch(spec1, 1280, 100, 32000, 0, None)
+        mel = self.norm_spec(torch.squeeze(mel, 0))
+        return spec, mel
+
+    def get_sid(self, sid):
+        sid = torch.LongTensor([int(sid)])
+        return sid
+
+    def __getitem__(self, index):
+        # with torch.no_grad():
+        return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
+
+    def __len__(self):
+        return len(self.audiopaths_sid_text)
+
+
+class TextAudioSpeakerCollateV4:
+    """Zero-pads model inputs and targets"""
+
+    def __init__(self, return_ids=False):
+        self.return_ids = return_ids
+
+    def __call__(self, batch):
+        """Collate's training batch from normalized text, audio and speaker identities
+        PARAMS
+        ------
+        batch: [text_normalized, spec_normalized, wav_normalized, sid]
+        """
+        # ssl, spec, wav,mel, text
+        # Right zero-pad all one-hot text sequences to max input length
+        _, ids_sorted_decreasing = torch.sort(torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True)
+        # (ssl, spec,mel, text)
+        max_ssl_len = max([x[0].size(2) for x in batch])
+        max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
+        max_spec_len = max([x[1].size(1) for x in batch])
+        max_spec_len = int(2 * ((max_spec_len // 2) + 1))
+        # max_wav_len = max([x[2].size(1) for x in batch])
+        max_text_len = max([x[3].size(0) for x in batch])
+
+        ssl_lengths = torch.LongTensor(len(batch))
+        spec_lengths = torch.LongTensor(len(batch))
+        text_lengths = torch.LongTensor(len(batch))
+        # wav_lengths = torch.LongTensor(len(batch))
+        mel_lengths = torch.LongTensor(len(batch))
+
+        spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
+        mel_padded = torch.FloatTensor(len(batch), batch[0][2].size(0), max_spec_len*2)
+        ssl_padded = torch.FloatTensor(len(batch), batch[0][0].size(1), max_ssl_len)
+        text_padded = torch.LongTensor(len(batch), max_text_len)
+        # wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
+
+        spec_padded.zero_()
+        mel_padded.zero_()
+        ssl_padded.zero_()
+        text_padded.zero_()
+        # wav_padded.zero_()
+
+        for i in range(len(ids_sorted_decreasing)):
+            row = batch[ids_sorted_decreasing[i]]
+            # ssl, spec, wav,mel, text
+            ssl = row[0]
+            ssl_padded[i, :, : ssl.size(2)] = ssl[0, :, :]
+            ssl_lengths[i] = ssl.size(2)
+
+            spec = row[1]
+            spec_padded[i, :, : spec.size(1)] = spec
+            spec_lengths[i] = spec.size(1)
+
+            # wav = row[2]
+            # wav_padded[i, :, :wav.size(1)] = wav
+            # wav_lengths[i] = wav.size(1)
+
+            mel = row[2]
+            mel_padded[i, :, : mel.size(1)] = mel
+            mel_lengths[i] = mel.size(1)
+
+            text = row[3]
+            text_padded[i, : text.size(0)] = text
+            text_lengths[i] = text.size(0)
+
+        # return ssl_padded, spec_padded,mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, wav_padded, wav_lengths,mel_lengths
+        return ssl_padded, spec_padded, mel_padded, ssl_lengths, spec_lengths, text_padded, text_lengths, mel_lengths
+
 
 class TextAudioSpeakerLoaderV3b(torch.utils.data.Dataset):
     """

GPT_SoVITS/module/mel_processing.py

@@ -38,89 +38,72 @@ hann_window = {}
 
 
 def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.0:
-        print("min value is ", torch.min(y))
-    if torch.max(y) > 1.0:
-        print("max value is ", torch.max(y))
+    if torch.min(y) < -1.2:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.2:
+        print('max value is ', torch.max(y))
 
     global hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    # wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    key = "%s-%s-%s-%s-%s" %(dtype_device,n_fft, sampling_rate, hop_size, win_size)
+    # if wnsize_dtype_device not in hann_window:
+    if key not in hann_window:
+        # hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+        hann_window[key] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
     y = y.squeeze(1)
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    # spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[key],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)
     return spec
 
 
 def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     global mel_basis
-    dtype_device = str(spec.dtype) + "_" + str(spec.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
+    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    # fmax_dtype_device = str(fmax) + '_' + dtype_device
+    key = "%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, fmin, fmax)
+    # if fmax_dtype_device not in mel_basis:
+    if key not in mel_basis:
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+        # mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+        mel_basis[key] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
+    # spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
+    spec = torch.matmul(mel_basis[key], spec)
     spec = spectral_normalize_torch(spec)
     return spec
 
 
+
 def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.0:
-        print("min value is ", torch.min(y))
-    if torch.max(y) > 1.0:
-        print("max value is ", torch.max(y))
+    if torch.min(y) < -1.2:
+        print('min value is ', torch.min(y))
+    if torch.max(y) > 1.2:
+        print('max value is ', torch.max(y))
 
     global mel_basis, hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
+    dtype_device = str(y.dtype) + '_' + str(y.device)
+    # fmax_dtype_device = str(fmax) + '_' + dtype_device
+    fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax)
+    # wnsize_dtype_device = str(win_size) + '_' + dtype_device
+    wnsize_dtype_device = fmax_dtype_device
     if fmax_dtype_device not in mel_basis:
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
         mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
     if wnsize_dtype_device not in hann_window:
         hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
 
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
+    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
     y = y.squeeze(1)
 
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-9)
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+
+    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)
 
     spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
     spec = spectral_normalize_torch(spec)

GPT_SoVITS/module/models.py

@@ -414,7 +414,7 @@ class Generator(torch.nn.Module):
         upsample_rates,
         upsample_initial_channel,
         upsample_kernel_sizes,
-        gin_channels=0,
+        gin_channels=0,is_bias=False,
     ):
         super(Generator, self).__init__()
         self.num_kernels = len(resblock_kernel_sizes)
@@ -442,7 +442,7 @@ class Generator(torch.nn.Module):
             for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
                 self.resblocks.append(resblock(ch, k, d))
 
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=is_bias)
         self.ups.apply(init_weights)
 
         if gin_channels != 0:
@@ -1173,7 +1173,7 @@ class SynthesizerTrnV3(nn.Module):
                 quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")  ##BCT
                 x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
         fea = self.bridge(x)
-        fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")  ##BCT
+        fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest")  ##BCT
         fea, y_mask_ = self.wns1(
             fea, mel_lengths, ge
         )  ##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate.
@@ -1196,9 +1196,9 @@ class SynthesizerTrnV3(nn.Module):
             ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
         y_lengths = torch.LongTensor([int(codes.size(2) * 2)]).to(codes.device)
         if speed == 1:
-            sizee = int(codes.size(2) * 2.5 * 1.5)
+            sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4))
         else:
-            sizee = int(codes.size(2) * 2.5 * 1.5 / speed) + 1
+            sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4) / speed) + 1
         y_lengths1 = torch.LongTensor([sizee]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
 
@@ -1207,7 +1207,7 @@ class SynthesizerTrnV3(nn.Module):
             quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")  ##BCT
         x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, speed)
         fea = self.bridge(x)
-        fea = F.interpolate(fea, scale_factor=1.875, mode="nearest")  ##BCT
+        fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest")  ##BCT
         ####more wn paramter to learn mel
         fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
         return fea, ge