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src/f5_tts/eval/eval_infer_batch.py

@@ -187,7 +187,7 @@ def main():
                 # Final result
                 for i, gen in enumerate(generated):
                     gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
-                    gen_mel_spec = gen.permute(0, 2, 1)
+                    gen_mel_spec = gen.permute(0, 2, 1).to(torch.float32)
                     if mel_spec_type == "vocos":
                         generated_wave = vocoder.decode(gen_mel_spec)
                     elif mel_spec_type == "bigvgan":
@@ -195,7 +195,7 @@ def main():
 
                     if ref_rms_list[i] < target_rms:
                         generated_wave = generated_wave * ref_rms_list[i] / target_rms
-                    torchaudio.save(f"{output_dir}/{utts[i]}.wav", generated_wave.squeeze(0).cpu(), target_sample_rate)
+                    torchaudio.save(f"{output_dir}/{utts[i]}.wav", generated_wave.cpu(), target_sample_rate)
 
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:

src/f5_tts/infer/infer_cli.py

@@ -109,13 +109,16 @@ ckpt_file = args.ckpt_file if args.ckpt_file else ""
 vocab_file = args.vocab_file if args.vocab_file else ""
 remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
 speed = args.speed
+
 wave_path = Path(output_dir) / "infer_cli_out.wav"
 # spectrogram_path = Path(output_dir) / "infer_cli_out.png"
-if args.vocoder_name == "vocos":
+
+vocoder_name = args.vocoder_name
+mel_spec_type = args.vocoder_name
+if vocoder_name == "vocos":
     vocoder_local_path = "../checkpoints/vocos-mel-24khz"
-elif args.vocoder_name == "bigvgan":
+elif vocoder_name == "bigvgan":
     vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
-mel_spec_type = args.vocoder_name
 
 vocoder = load_vocoder(vocoder_name=mel_spec_type, is_local=args.load_vocoder_from_local, local_path=vocoder_local_path)
 
@@ -125,19 +128,20 @@ if model == "F5-TTS":
     model_cls = DiT
     model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
     if ckpt_file == "":
-        if args.vocoder_name == "vocos":
+        if vocoder_name == "vocos":
             repo_name = "F5-TTS"
             exp_name = "F5TTS_Base"
             ckpt_step = 1200000
             ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
             # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
-        elif args.vocoder_name == "bigvgan":
+        elif vocoder_name == "bigvgan":
             repo_name = "F5-TTS"
             exp_name = "F5TTS_Base_bigvgan"
             ckpt_step = 1250000
             ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
 
 elif model == "E2-TTS":
+    assert vocoder_name == "vocos", "E2-TTS only supports vocoder vocos"
     model_cls = UNetT
     model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
     if ckpt_file == "":
@@ -146,15 +150,10 @@ elif model == "E2-TTS":
         ckpt_step = 1200000
         ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
         # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
-    elif args.vocoder_name == "bigvgan":  # TODO: need to test
-        repo_name = "F5-TTS"
-        exp_name = "F5TTS_Base_bigvgan"
-        ckpt_step = 1250000
-        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.pt"))
 
 
 print(f"Using {model}...")
-ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=args.vocoder_name, vocab_file=vocab_file)
+ema_model = load_model(model_cls, model_cfg, ckpt_file, mel_spec_type=mel_spec_type, vocab_file=vocab_file)
 
 
 def main_process(ref_audio, ref_text, text_gen, model_obj, mel_spec_type, remove_silence, speed):

src/f5_tts/infer/speech_edit.py

@@ -187,5 +187,5 @@ with torch.inference_mode():
         generated_wave = generated_wave * rms / target_rms
 
     save_spectrogram(gen_mel_spec[0].cpu().numpy(), f"{output_dir}/speech_edit_out.png")
-    torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave.squeeze(0).cpu(), target_sample_rate)
+    torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave.cpu(), target_sample_rate)
     print(f"Generated wav: {generated_wave.shape}")