F5 tts (#59)

* init tts/F5-TTS

* epic hax to download ckpts

* clear default reference audio/text from inference vars

* allow blank inputs for inference-cli to just pull checkpoints without running inference

* f5-tts github workflow to publish docker images

tts/F5-TTS/scripts/count_max_epoch.py

@@ -0,0 +1,32 @@
+'''ADAPTIVE BATCH SIZE'''
+print('Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in')
+print('  -> least padding, gather wavs with accumulated frames in a batch\n')
+
+# data
+total_hours = 95282
+mel_hop_length = 256
+mel_sampling_rate = 24000
+
+# target
+wanted_max_updates = 1000000
+
+# train params
+gpus = 8
+frames_per_gpu = 38400  # 8 * 38400 = 307200
+grad_accum = 1
+
+# intermediate
+mini_batch_frames = frames_per_gpu * grad_accum * gpus
+mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+updates_per_epoch = total_hours / mini_batch_hours
+steps_per_epoch = updates_per_epoch * grad_accum
+
+# result
+epochs = wanted_max_updates / updates_per_epoch
+print(f"epochs should be set to: {epochs:.0f} ({epochs/grad_accum:.1f} x gd_acum {grad_accum})")
+print(f"progress_bar should show approx. 0/{updates_per_epoch:.0f} updates")
+print(f"                      or approx. 0/{steps_per_epoch:.0f} steps")
+
+# others
+print(f"total {total_hours:.0f} hours")
+print(f"mini-batch of {mini_batch_frames:.0f} frames, {mini_batch_hours:.2f} hours per mini-batch")