Merge pull request #3103 from coqui-ai/fix_xttsv1.1_again

Second round of issue fixing for XTTS v1.1

TTS/tts/layers/xtts/tokenizer.py

@@ -483,20 +483,18 @@ def preprocess_text(self, txt, lang):
             if lang == "zh-cn":
                 txt = chinese_transliterate(txt)
         elif lang == "ja":
-            assert txt[:4] == "[ja]", "Japanese speech should start with the [ja] token."
-            txt = txt[4:]
             if self.katsu is None:
                 import cutlet
                 self.katsu = cutlet.Cutlet()
             txt = japanese_cleaners(txt, self.katsu)
-            txt = "[ja]" + txt
         else:
             raise NotImplementedError()
         return txt
 
     def encode(self, txt, lang):
         if self.preprocess:
             txt = self.preprocess_text(txt, lang)
+        txt = f"[{lang}]{txt}"
         txt = txt.replace(" ", "[SPACE]")
         return self.tokenizer.encode(txt).ids
 

TTS/tts/models/xtts.py

@@ -5,6 +5,7 @@
 import torch
 import torch.nn.functional as F
 import torchaudio
+import librosa
 from coqpit import Coqpit
 
 from TTS.tts.layers.tortoise.audio_utils import denormalize_tacotron_mel, wav_to_univnet_mel
@@ -21,34 +22,6 @@
 init_stream_support()
 
 
-def load_audio(audiopath, sr=22050):
-    """
-    Load an audio file from disk and resample it to the specified sampling rate.
-
-    Args:
-        audiopath (str): Path to the audio file.
-        sr (int): Target sampling rate.
-
-    Returns:
-        Tensor: Audio waveform tensor with shape (1, T), where T is the number of samples.
-    """
-    audio, sampling_rate = torchaudio.load(audiopath)
-
-    if len(audio.shape) > 1:
-        if audio.shape[0] < 5:
-            audio = audio[0]
-        else:
-            assert audio.shape[1] < 5
-            audio = audio[:, 0]
-
-    if sampling_rate != sr:
-        resampler = torchaudio.transforms.Resample(sampling_rate, sr)
-        audio = resampler(audio)
-
-    audio = audio.clamp_(-1, 1)
-    return audio.unsqueeze(0)
-
-
 def wav_to_mel_cloning(
     wav, mel_norms_file="../experiments/clips_mel_norms.pth", mel_norms=None, device=torch.device("cpu")
 ):
@@ -376,32 +349,29 @@ def device(self):
         return next(self.parameters()).device
 
     @torch.inference_mode()
-    def get_gpt_cond_latents(self, audio_path: str, length: int = 3):
+    def get_gpt_cond_latents(self, audio, sr, length: int = 3):
         """Compute the conditioning latents for the GPT model from the given audio.
 
         Args:
             audio_path (str): Path to the audio file.
             length (int): Length of the audio in seconds. Defaults to 3.
         """
 
-        audio = load_audio(audio_path)
-        audio = audio[:, : 22050 * length]
-        mel = wav_to_mel_cloning(audio, mel_norms=self.mel_stats.cpu())
+        audio_22k = torchaudio.functional.resample(audio, sr, 22050)
+        audio_22k = audio_22k[:, : 22050 * length]
+        mel = wav_to_mel_cloning(audio_22k, mel_norms=self.mel_stats.cpu())
         cond_latent = self.gpt.get_style_emb(mel.to(self.device))
         return cond_latent.transpose(1, 2)
 
     @torch.inference_mode()
-    def get_diffusion_cond_latents(
-        self,
-        audio_path,
-    ):
+    def get_diffusion_cond_latents(self, audio, sr):
         from math import ceil
 
         diffusion_conds = []
         CHUNK_SIZE = 102400
-        audio = load_audio(audio_path, 24000)
-        for chunk in range(ceil(audio.shape[1] / CHUNK_SIZE)):
-            current_sample = audio[:, chunk * CHUNK_SIZE : (chunk + 1) * CHUNK_SIZE]
+        audio_24k = torchaudio.functional.resample(audio, sr, 24000)
+        for chunk in range(ceil(audio_24k.shape[1] / CHUNK_SIZE)):
+            current_sample = audio_24k[:, chunk * CHUNK_SIZE : (chunk + 1) * CHUNK_SIZE]
             current_sample = pad_or_truncate(current_sample, CHUNK_SIZE)
             cond_mel = wav_to_univnet_mel(
                 current_sample.to(self.device),
@@ -414,27 +384,38 @@ def get_diffusion_cond_latents(
         return diffusion_latent
 
     @torch.inference_mode()
-    def get_speaker_embedding(self, audio_path):
-        audio = load_audio(audio_path, self.hifigan_decoder.speaker_encoder_audio_config["sample_rate"])
-        speaker_embedding = (
-            self.hifigan_decoder.speaker_encoder.forward(audio.to(self.device), l2_norm=True)
-            .unsqueeze(-1)
-            .to(self.device)
-        )
-        return speaker_embedding
-
+    def get_speaker_embedding(self, audio, sr):
+        audio_16k = torchaudio.functional.resample(audio, sr, 16000)
+        return self.hifigan_decoder.speaker_encoder.forward(
+            audio_16k.to(self.device), l2_norm=True
+        ).unsqueeze(-1).to(self.device)
+
+    @torch.inference_mode()
     def get_conditioning_latents(
         self,
         audio_path,
-        gpt_cond_len=3,
-    ):
+        gpt_cond_len=6,
+        max_ref_length=10,
+        librosa_trim_db=None,
+        sound_norm_refs=False,
+    ):  
         speaker_embedding = None
         diffusion_cond_latents = None
-        if self.args.use_hifigan:
-            speaker_embedding = self.get_speaker_embedding(audio_path)
+
+        audio, sr = torchaudio.load(audio_path)
+        audio = audio[:, : sr * max_ref_length].to(self.device)
+        if audio.shape[0] > 1:
+            audio = audio.mean(0, keepdim=True)
+        if sound_norm_refs:
+            audio = (audio / torch.abs(audio).max()) * 0.75
+        if librosa_trim_db is not None:
+            audio = librosa.effects.trim(audio, top_db=librosa_trim_db)[0]
+
+        if self.args.use_hifigan or self.args.use_ne_hifigan:
+            speaker_embedding = self.get_speaker_embedding(audio, sr)
         else:
-            diffusion_cond_latents = self.get_diffusion_cond_latents(audio_path)
-        gpt_cond_latents = self.get_gpt_cond_latents(audio_path, length=gpt_cond_len)  # [1, 1024, T]
+            diffusion_cond_latents = self.get_diffusion_cond_latents(audio, sr)
+        gpt_cond_latents = self.get_gpt_cond_latents(audio, sr, length=gpt_cond_len)  # [1, 1024, T]
         return gpt_cond_latents, diffusion_cond_latents, speaker_embedding
 
     def synthesize(self, text, config, speaker_wav, language, **kwargs):
@@ -494,7 +475,7 @@ def full_inference(
         repetition_penalty=2.0,
         top_k=50,
         top_p=0.85,
-        gpt_cond_len=4,
+        gpt_cond_len=6,
         do_sample=True,
         # Decoder inference
         decoder_iterations=100,
@@ -531,7 +512,7 @@ def full_inference(
                 (aka boring) outputs. Defaults to 0.8.
 
             gpt_cond_len: (int) Length of the audio used for cloning. If audio is shorter, then audio length is used
-                else the first `gpt_cond_len` secs is used. Defaults to 3 seconds.
+                else the first `gpt_cond_len` secs is used. Defaults to 6 seconds.
 
             decoder_iterations: (int) Number of diffusion steps to perform. [0,4000]. More steps means the network has
                 more chances to iteratively refine the output, which should theoretically mean a higher quality output.
@@ -610,7 +591,7 @@ def inference(
         decoder="hifigan",
         **hf_generate_kwargs,
     ):
-        text = f"[{language}]{text.strip().lower()}"
+        text = text.strip().lower()
         text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device)
 
         assert (
@@ -722,7 +703,7 @@ def inference_stream(
         assert hasattr(
             self, "hifigan_decoder"
         ), "`inference_stream` requires use_hifigan to be set to true in the config.model_args, diffusion is too slow to stream."
-        text = f"[{language}]{text.strip().lower()}"
+        text = text.strip().lower()
         text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device)
 
         fake_inputs = self.gpt.compute_embeddings(